In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be ut...In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.展开更多
BACKGROUND Colorectal cancer has a high incidence and mortality rate,and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise.In recent years,computer-aided detection(CADe)systems h...BACKGROUND Colorectal cancer has a high incidence and mortality rate,and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise.In recent years,computer-aided detection(CADe)systems have been increasingly integrated into colonoscopy to improve detection accuracy.However,while most studies have focused on adenoma detection rate(ADR)as the primary outcome,the more sensitive adenoma miss rate(AMR)has been less frequently analyzed.AIM To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.METHODS A comprehensive literature search was conducted in PubMed,Embase,and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2,2024.Statistical analyses were performed to compare outcomes between groups,and potential publication bias was assessed using funnel plots.The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations,Assessment,Development,and Evaluation approach.RESULTS Five studies comprising 1624 patients met the inclusion criteria.AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group(147/927,15.9%vs 345/960,35.9%;P<0.01).However,CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or≥10 mm.The polyp miss rate(PMR)was also lower in the CADe-assisted group[odds ratio(OR),0.35;95% confidence interval(CI):0.23-0.52;P<0.01].While the overall ADR did not differ significantly between groups,the ADR during the first-pass examination was higher in the CADe-assisted group(OR,1.37;95%CI:1.10-1.69;P=0.004).The level of evidence for the included randomized controlled trials was graded as moderate.CONCLUSION CADe can significantly reduce AMR and PMR while improving ADR during initial detection,demonstrating its potential to enhance colonoscopy performance.These findings highlight the value of CADe in improving the detection of colorectal neoplasms,particularly small and histologically distinct adenomas.展开更多
Screening colonoscopy with adenoma removal is the gold standard strategy to reduce colorectal cancer(CRC)incidence.Nevertheless,it remains an imperfect tool as nearly Twenty-five percent of adenomas can be missed duri...Screening colonoscopy with adenoma removal is the gold standard strategy to reduce colorectal cancer(CRC)incidence.Nevertheless,it remains an imperfect tool as nearly Twenty-five percent of adenomas can be missed during inspection by experienced endoscopists.Missed lesions are one of the primary reasons for post colonoscopy CRC and are associated with a significant variability in adenoma detection rate(ADR),which is the most important quality indicator for colonoscopy.Increasing ADR unquestionably decreases carcinoma miss rate.Simple measures to improve ADR include among others slower withdrawal time and position change.The introduction of optical imaging innovations has improved mucosal visualization.Moreover,auxiliary devices attached to the colonoscope tip have been introduced,aiming to improve lumen visualization by flattening the folds and revealing lesions hidden in blind spots,thereby increasing ADR.Digital image analysis using artificial intelligence is the latest approach to polyp detection.All of the above approaches have been separately evaluated concerning their effect in ADR;however,it has not been thoroughly investigated whether any benefit exists from their combined use.We aim to review the available data on the efficacy of each technique/technology and whether their combination offers any additional benefit while remaining cost-effective.展开更多
Computer-aided diagnosis(CAD)can detect tuberculosis(TB)cases,providing radiologists with more accurate and efficient diagnostic solutions.Various noise information in TB chest X-ray(CXR)images is a major challenge in...Computer-aided diagnosis(CAD)can detect tuberculosis(TB)cases,providing radiologists with more accurate and efficient diagnostic solutions.Various noise information in TB chest X-ray(CXR)images is a major challenge in this classification task.This study aims to propose a model with high performance in TB CXR image detection named multi-scale input mirror network(MIM-Net)based on CXR image symmetry,which consists of a multi-scale input feature extraction network and mirror loss.The multi-scale image input can enhance feature extraction,while the mirror loss can improve the network performance through self-supervision.We used a publicly available TB CXR image classification dataset to evaluate our proposed method via 5-fold cross-validation,with accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and area under curve(AUC)of 99.67%,100%,99.60%,99.80%,100%,and 0.9999,respectively.Compared to other models,MIM-Net performed best in all metrics.Therefore,the proposed MIM-Net can effectively help the network learn more features and can be used to detect TB in CXR images,thus assisting doctors in diagnosing.展开更多
CT colonography （CTC） is a non-invasive screening technique for the detection of eolorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection （CAD） for CTC refers to a...CT colonography （CTC） is a non-invasive screening technique for the detection of eolorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection （CAD） for CTC refers to a scheme which automatically detects colorectal polyps and masses in CT images of the colon. It has the potential to increase radiologists＇ detection performance and greatly shorten the detection time. Over the years, technical developments have advanced CAD for CTC substantially. In this paper, key techniques used in CAD for polyp detection are reviewed. Illustrations about the performance of existing CAD schemes show their relatively high sensitivity and low false positive rate. However, these CAD schemes are still suffering from technical or clinical problems. Some existing challenges faced by CAD are also pointed out at the end of this paper.展开更多
Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators...Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.展开更多
In this thesis, a strategy realizing the computer-aided detection (CAD) of the epileptic waves in EEG isintroduced. The expert criterion, continuous wavelet transformation, neural networks, and characteristic paramete...In this thesis, a strategy realizing the computer-aided detection (CAD) of the epileptic waves in EEG isintroduced. The expert criterion, continuous wavelet transformation, neural networks, and characteristic parametermeasuremente these modern signa1 processing weapons were synthesized togetLher to form a so-called multi-method.It was estimated that the advantages of all the powerful techniques could be exploited systematically. Therefore, theCAD’s capacities in the long-term monitoring, trCaAnent and control of epilepsy might be enhanced. In this strategy,the raw EEG signals were uniformed and the expelt criterion were applied to discard most of aItifacts in them at first,and then the signals were pre-processed by continuous wavelet transformation. Some characteristic parameters wereextracted from the raw signals and the pre-processed ones. Consequently groups of eighteen parameters were sent totrain or test BP networks. By applying this theme a correct-detection rate of 84.3% for spike and sharp waves, and88.9% for sPike and sharp slow waves were obtained. In the next step, some non-linear tools wtll also be equippedwith the CAD system.展开更多
Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologi...Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologists’examination procedure,are built upon computer tomography(CT)images with feature extraction for detection and diagnosis.Human visual perception in CT image is reconstructed from sinogram,which is the original raw data acquired from CT scanner.In this work,different from the conventional image based CADe system,we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain.Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain,we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram.The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database,with each case having at least one juxtapleural nodule annotation.Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve(AUC)of receiver operating characteristic based on sinogram alone,comparing to 0.89 based on CT image alone.Moreover,a combination of sinogram and CT image could further improve the value of AUC to 0.92.This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.展开更多
This study explores an automated framework to assist the recognition of hemorrhage traces and bleeding lesions in video streams of small bowel capsule endoscopy (SBCE). The proposed methodology aims to achieve fast im...This study explores an automated framework to assist the recognition of hemorrhage traces and bleeding lesions in video streams of small bowel capsule endoscopy (SBCE). The proposed methodology aims to achieve fast image control (<10 minutes), save valuable time of the physicians, and enable high performance diagnosis. A specialized elimination algorithm excludes all identical consecutive frames by utilizing the difference of gray levels in pixel luminance. An image filtering algorithm is proposed based on an experimentally calculated bleeding index and blood-color chart, which inspects all remaining frames of the footage and identifies pixels that reflect active or potential hemorrhage in color. The bleeding index and blood-color chart are estimated of the chromatic thresholds in RGB and HSV color spaces, and have been extracted after experimenting with more than 3200 training images, derived from 99 videos of a pool of 138 patients. The dataset has been provided by a team of expert gastroenterologist surgeons, who have also evaluated the results. The proposed algorithms are tested on a set of more than 1000 selected frame samples from the entire 39 testing videos, to a prevalence of 50% pathologic frames (balanced dataset). The frame elimination of identical and consecutive frames achieved a reduction of 36% of total frames. The best statistical performance for diagnosis of positive pathological frames from a video stream is achieved by utilizing masks in the HSV color model, with sensitivity up to 99%, precision 94.41% to a prevalence of 50%, accuracy up to 96.1%, FNR 1%, FPR 6.8%. The estimated blood-color chart will be clinically validated and used in feature extraction schemes supporting machine learning ML algorithms to improve the localization potential.展开更多
Background:Computer-aided detection(CAD)software has been introduced to automatically interpret digital chest X-rays.This study aimed to evaluate the performance of CAD software(JF CXR-1 v3.0,which was developed by a ...Background:Computer-aided detection(CAD)software has been introduced to automatically interpret digital chest X-rays.This study aimed to evaluate the performance of CAD software(JF CXR-1 v3.0,which was developed by a domestic Hi-tech enterprise)in tuberculosis(TB)case finding in China.Methods:In 2019,we conducted an internal evaluation of the performance of JF CXR-1 v3.0 by reading standard images annotated by a panel of experts.In 2020,using the reading results of chest X-rays by a panel of experts as the reference standard,we conducted an on-site prospective study to evaluate the performance of JF CXR-1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County,Henan Province.Results:Internal assessment results based on 277 standard images showed that JF CXR-1 v3.0 had a sensitivity of 85.94%(95%confidence interval[CI]:77.42%,94.45%)and a specificity of 74.65%(95%CI:68.81%,80.49%)to distinguish active TB from other imaging conditions.In the on-site evaluation phase,images from 3705 outpatients who underwent chest X-ray detection were read by JF CXR-1 v3.0 and local radiologists in parallel.The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89%(95%CI:22.33%,43.46%)and a specificity of 99.28%(95%CI:99.01%,99.56%),while JF CXR-1 v3.0 showed a significantly higher sensitivity of 92.11%(95%CI:86.04%,98.17%)(p<0.05)and maintained high specificity at 94.54%(95%CI:93.81%,95.28%).Conclusions:CAD software could play a positive role in improving the TB case finding capability of township health centers.展开更多
Purpose: Surgical templates produced by digital simulation and CAD/CAM allow for three-dimensional control of implant placement. However, due to clinical limitations, there are complications during the use of the temp...Purpose: Surgical templates produced by digital simulation and CAD/CAM allow for three-dimensional control of implant placement. However, due to clinical limitations, there are complications during the use of the template. The purpose of this study was to summarize the complications associated with the use of surgical templates for static computer-aided implant surgery. Methods: Complications were collected during the observation period, and then their implant sites were reanalyzed with simulation software. Results: There were 104 cases during the observation period, 5 cases had complications. Mechanical complications were observed in four cases, including three cases in which the frame of the template fractured during implant placement surgery and one case in which the sleeve fell off the surgical template. In one case, there was an error in the planned position. All cases were mandibular molar cases, and all cases of frame fracture were at the free end defect site. All cases had a Hounsfield unit of more than 700 at the implant site, and some of them had a significantly small jaw opening. Conclusion: Although the spread of CAD/CAM surgical templates has made it possible to avoid problems caused by the position of the implant, it has been difficult to avoid fractures in cases of mandibular free end defects with high Hounsfield unit.展开更多
Liver fibrosis is an important pathological precondition for hepatocellular carcinoma.The degree of hepatic fibrosis is positively correlated with liver cancer.Liver fibrosis is a series of pathological and physiologi...Liver fibrosis is an important pathological precondition for hepatocellular carcinoma.The degree of hepatic fibrosis is positively correlated with liver cancer.Liver fibrosis is a series of pathological and physiological process related to liver cell necrosis and degeneration after chronic liver injury,which finally leads to extracellular matrix and collagen deposition.The early detection and precise staging of fibrosis and cirrhosis are very important for early diagnosis and timely initiation of appropriate therapeutic regimens.The risk of severe liver fibrosis finally progressing to liver carcinoma is＆gt;50%.It is known that biopsy is the gold standard for the diagnosis and staging of liver fibrosis.However,this method has some limitations,such as the potential for pain,sampling variability,and low patient acceptance.Furthermore,the necessity of obtaining a tissue diagnosis of liver fibrosis still remains controversial.An increasing number of reliable non-invasive approaches are now available that are widely applied in clinical practice,mostly in cases of viral hepatitis,resulting in a significantly decreased need for liver biopsy.In fact,the noninvasive detection and evaluation of liver cirrhosis now has good accuracy due to current serum markers,ultrasound imaging,and magnetic resonance imaging quantification techniques.A prominent advantage of the non-invasive detection and assessment of liver fibrosis is that liver fibrosis can be monitored repeatedly and easily in the same patient.Serum biomarkers have the advantages of high applicability（〉95%）and good reproducibility.However,their results can be influenced by different patient conditions because none of these markers are liver-specific.The most promising techniques appear to be transient elastography and magnetic resonance elastography because they provide reliable results for the detection of fibrosis in the advanced stages,and future developments promise to increase the reliability and accuracy of the staging of hepatic fibrosis.This article aims to describe the recent progress in the development of non-invasive assessment methods for the staging of liver fibrosis,with a special emphasize on computer-aided quantitative and deep learning methods.展开更多
Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules,the pote...Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules,the potential precursors to lung cancer, is paramount. In this paper, a computer-aided lung nodule detection system using 3D deep convolutional neural networks(CNNs) is developed. The first multi-scale 11-layer 3D fully convolutional neural network(FCN) is used for screening all lung nodule candidates. Considering relative small sizes of lung nodules and limited memory, the input of the FCN consists of 3D image patches rather than of whole images. The candidates are further classified in the second CNN to get the final result. The proposed method achieves high performance in the LUNA16 challenge and demonstrates the effectiveness of using 3D deep CNNs for lung nodule detection.展开更多
Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or precancerous lesions and the capacity to remove them intra-procedural...Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or precancerous lesions and the capacity to remove them intra-procedurally.Computeraided detection and diagnosis(CAD),thanks to the brand new developed innovations of artificial intelligence,and especially deep-learning techniques,leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy.The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate,and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality.Furthermore,a significant reduction in costs is also expected.In addition,the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule.The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy,as it is reported in literature,addressing evidence,limitations,and future prospects.展开更多
Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens t...Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs.Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health.As their drinking increases,they become dependent on alcohol and it affects their daily lives.Therefore,it is important to recognize the dangers of alcohol abuse and to stop drinking as soon as possible.To assist physicians in the diagnosis of patients with alcoholism,we provide a novel alcohol detection system by extracting image features of wavelet energy entropy from magnetic resonance imaging(MRI)combined with a linear regression classifier.Compared with the latest method,the 10-fold cross-validation experiment showed excellent results,including sensitivity 91.54±1.47%,specificity 93.66±1.34%,Precision 93.45±1.27%,accuracy 92.61±0.81%,F1 score 92.48±0.83%and MCC 85.26±1.62%.展开更多
The general computer-aided design （CAD） software cannot meet the mould design requirement of the autoclave process for composites, because many parameters such as temperature and pressure should be considered in the...The general computer-aided design （CAD） software cannot meet the mould design requirement of the autoclave process for composites, because many parameters such as temperature and pressure should be considered in the mould design process, in addition to the material and geometry of the part. A framed-mould computer-aided design system （FMCAD） used in the autoclave moulding process is proposed in this paper. A function model of the software is presented, in which influence factors such as part structure, mould structure, and process parameters are considered; a design model of the software is established using object oriented （O-O） technology to integrate the stiffness calculation, temperature field calculation, and deformation field calculation of mould in the design, and in the design model, a hybrid model of mould based on calculation feature and form feature is presented to support those calculations. A prototype system is developed, in which a mould design process wizard is built to integrate the input information, calculation, analysis, data storage, display, and design results of mould design. Finally, three design examples are used to verify the prototype.展开更多
AIM: Variation in structure-related components in plant products prompted the trend to establish methods, using multiple or total analog analysis, for their effective quality control. However, the general use of routi...AIM: Variation in structure-related components in plant products prompted the trend to establish methods, using multiple or total analog analysis, for their effective quality control. However, the general use of routine quality control is restricted by the limited availability of reference substances. Using an easily available single marker as a reference standard to determine multiple or total analogs should be a practical option. METHOD: In this study, the Ultra-HPLC method was used for the baseline separation of the main components in ginseng extracts. Using a plant chemical component database, ginsenosides in ginseng extracts were identified by Ultra-HPLC-MS analysis. The charged aerosol detection(CAD) system with post-column compensation of the gradient generates a similar response for identical amounts of different analytes, and thus, the content of each ginsenoside in ginseng extracts was determined by comparing the analyte peak area with the reference standard(determination of total analogs by single marker, DTSM). The total ginsenoside content was determined by the summation of reference standard and other ginsenoside components. RESULTS: The results showed that DTSM approaches were available for the determination of total ginsenosides in a high purity ginseng extract because of the removal of impurities. In contrast, DTSM approaches might be suitable for determination of multiple ginsenosides without interference from impurities in the crude ginseng extract. CONCLUSION: Future practical studies similar to the present study should be conducted to verify that DTSM approaches based on CAD with post-column inverse gradient for uniform response are ideal for the quality control of plant products.展开更多
With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease globally.Breast cancer is the most widely affected cancer worldwide,with an increased death...With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease globally.Breast cancer is the most widely affected cancer worldwide,with an increased death rate percentage.Due to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast cancer.Early detection of breast cancer will reduce the death rate worldwide.The early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive rate.This paper proposed an efficient and optimized deep learning-based feature selection approach with this consideration.This model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm rate.Transfer learning is used in the extraction of features initially.Na ext,a convolution neural network,is used to extract the features.The two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant features.The optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant images.The feature extraction and classification process used two datasets,breast,and MIAS.Compared to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.展开更多
Due to small size and high occult,metacarpophalangeal fracturediagnosis displays a low accuracy in terms of fracture detection and locationin X-ray images.To efficiently detect metacarpophalangeal fractures on Xrayima...Due to small size and high occult,metacarpophalangeal fracturediagnosis displays a low accuracy in terms of fracture detection and locationin X-ray images.To efficiently detect metacarpophalangeal fractures on Xrayimages as the second opinion for radiologists,we proposed a novel onestageneural network namedMPFracNet based onRetinaNet.InMPFracNet,a deformable bottleneck block(DBB)was integrated into the bottleneckto better adapt to the geometric variation of the fractures.Furthermore,an integrated feature fusion module(IFFM)was employed to obtain morein-depth semantic and shallow detail features.Specifically,Focal Loss andBalanced L1 Loss were introduced to respectively attenuate the imbalancebetween positive and negative classes and the imbalance between detectionand location tasks.We assessed the proposed model on the test set andachieved an AP of 80.4%for the metacarpophalangeal fracture detection.To estimate the detection performance for fractures with different difficulties,the proposed model was tested on the subsets of metacarpal,phalangeal andtiny fracture test sets and achieved APs of 82.7%,78.5%and 74.9%,respectively.Our proposed framework has state-of-the-art performance for detectingmetacarpophalangeal fractures,which has a strong potential application valuein practical clinical environments.展开更多
The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is ...The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.展开更多
文摘In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.
文摘BACKGROUND Colorectal cancer has a high incidence and mortality rate,and the effectiveness of routine colonoscopy largely depends on the endoscopist’s expertise.In recent years,computer-aided detection(CADe)systems have been increasingly integrated into colonoscopy to improve detection accuracy.However,while most studies have focused on adenoma detection rate(ADR)as the primary outcome,the more sensitive adenoma miss rate(AMR)has been less frequently analyzed.AIM To evaluate the effectiveness of CADe in colonoscopy and assess the advantages of AMR over ADR.METHODS A comprehensive literature search was conducted in PubMed,Embase,and the Cochrane Central Register of Controlled Trials using predefined search strategies to identify relevant studies published up to August 2,2024.Statistical analyses were performed to compare outcomes between groups,and potential publication bias was assessed using funnel plots.The quality of the included studies was evaluated using the Cochrane Risk of Bias tool and the Grading of Recommendations,Assessment,Development,and Evaluation approach.RESULTS Five studies comprising 1624 patients met the inclusion criteria.AMR was significantly lower in the CADe-assisted group than in the routine colonoscopy group(147/927,15.9%vs 345/960,35.9%;P<0.01).However,CADe did not provide a significant advantage in detecting advanced adenomas or lesions measuring 6-9 mm or≥10 mm.The polyp miss rate(PMR)was also lower in the CADe-assisted group[odds ratio(OR),0.35;95% confidence interval(CI):0.23-0.52;P<0.01].While the overall ADR did not differ significantly between groups,the ADR during the first-pass examination was higher in the CADe-assisted group(OR,1.37;95%CI:1.10-1.69;P=0.004).The level of evidence for the included randomized controlled trials was graded as moderate.CONCLUSION CADe can significantly reduce AMR and PMR while improving ADR during initial detection,demonstrating its potential to enhance colonoscopy performance.These findings highlight the value of CADe in improving the detection of colorectal neoplasms,particularly small and histologically distinct adenomas.
文摘Screening colonoscopy with adenoma removal is the gold standard strategy to reduce colorectal cancer(CRC)incidence.Nevertheless,it remains an imperfect tool as nearly Twenty-five percent of adenomas can be missed during inspection by experienced endoscopists.Missed lesions are one of the primary reasons for post colonoscopy CRC and are associated with a significant variability in adenoma detection rate(ADR),which is the most important quality indicator for colonoscopy.Increasing ADR unquestionably decreases carcinoma miss rate.Simple measures to improve ADR include among others slower withdrawal time and position change.The introduction of optical imaging innovations has improved mucosal visualization.Moreover,auxiliary devices attached to the colonoscope tip have been introduced,aiming to improve lumen visualization by flattening the folds and revealing lesions hidden in blind spots,thereby increasing ADR.Digital image analysis using artificial intelligence is the latest approach to polyp detection.All of the above approaches have been separately evaluated concerning their effect in ADR;however,it has not been thoroughly investigated whether any benefit exists from their combined use.We aim to review the available data on the efficacy of each technique/technology and whether their combination offers any additional benefit while remaining cost-effective.
基金supported by the Joint Fund of the Ministry of Education for Equipment Pre-research(No.8091B0203)National Key Research and Development Program of China(No.2020YFC2008700)。
文摘Computer-aided diagnosis(CAD)can detect tuberculosis(TB)cases,providing radiologists with more accurate and efficient diagnostic solutions.Various noise information in TB chest X-ray(CXR)images is a major challenge in this classification task.This study aims to propose a model with high performance in TB CXR image detection named multi-scale input mirror network(MIM-Net)based on CXR image symmetry,which consists of a multi-scale input feature extraction network and mirror loss.The multi-scale image input can enhance feature extraction,while the mirror loss can improve the network performance through self-supervision.We used a publicly available TB CXR image classification dataset to evaluate our proposed method via 5-fold cross-validation,with accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and area under curve(AUC)of 99.67%,100%,99.60%,99.80%,100%,and 0.9999,respectively.Compared to other models,MIM-Net performed best in all metrics.Therefore,the proposed MIM-Net can effectively help the network learn more features and can be used to detect TB in CXR images,thus assisting doctors in diagnosing.
基金the National Natural Science Foundation of China(No.813716234)the National Basic Research Program(973) of China(No.2010CB834302)the Shanghai Jiao Tong University Medical Engineering Cross Research Funds(Nos.YG2013MS30 and YG2011MS51)
文摘CT colonography （CTC） is a non-invasive screening technique for the detection of eolorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection （CAD） for CTC refers to a scheme which automatically detects colorectal polyps and masses in CT images of the colon. It has the potential to increase radiologists＇ detection performance and greatly shorten the detection time. Over the years, technical developments have advanced CAD for CTC substantially. In this paper, key techniques used in CAD for polyp detection are reviewed. Illustrations about the performance of existing CAD schemes show their relatively high sensitivity and low false positive rate. However, these CAD schemes are still suffering from technical or clinical problems. Some existing challenges faced by CAD are also pointed out at the end of this paper.
文摘Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.
文摘In this thesis, a strategy realizing the computer-aided detection (CAD) of the epileptic waves in EEG isintroduced. The expert criterion, continuous wavelet transformation, neural networks, and characteristic parametermeasuremente these modern signa1 processing weapons were synthesized togetLher to form a so-called multi-method.It was estimated that the advantages of all the powerful techniques could be exploited systematically. Therefore, theCAD’s capacities in the long-term monitoring, trCaAnent and control of epilepsy might be enhanced. In this strategy,the raw EEG signals were uniformed and the expelt criterion were applied to discard most of aItifacts in them at first,and then the signals were pre-processed by continuous wavelet transformation. Some characteristic parameters wereextracted from the raw signals and the pre-processed ones. Consequently groups of eighteen parameters were sent totrain or test BP networks. By applying this theme a correct-detection rate of 84.3% for spike and sharp waves, and88.9% for sPike and sharp slow waves were obtained. In the next step, some non-linear tools wtll also be equippedwith the CAD system.
基金This work was partially supported by the NIH/NCI grant#CA206171 of the National Cancer Institute and the PSC-CUNY award 62310–0050.
文摘Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologists’examination procedure,are built upon computer tomography(CT)images with feature extraction for detection and diagnosis.Human visual perception in CT image is reconstructed from sinogram,which is the original raw data acquired from CT scanner.In this work,different from the conventional image based CADe system,we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain.Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain,we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram.The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database,with each case having at least one juxtapleural nodule annotation.Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve(AUC)of receiver operating characteristic based on sinogram alone,comparing to 0.89 based on CT image alone.Moreover,a combination of sinogram and CT image could further improve the value of AUC to 0.92.This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.
文摘This study explores an automated framework to assist the recognition of hemorrhage traces and bleeding lesions in video streams of small bowel capsule endoscopy (SBCE). The proposed methodology aims to achieve fast image control (<10 minutes), save valuable time of the physicians, and enable high performance diagnosis. A specialized elimination algorithm excludes all identical consecutive frames by utilizing the difference of gray levels in pixel luminance. An image filtering algorithm is proposed based on an experimentally calculated bleeding index and blood-color chart, which inspects all remaining frames of the footage and identifies pixels that reflect active or potential hemorrhage in color. The bleeding index and blood-color chart are estimated of the chromatic thresholds in RGB and HSV color spaces, and have been extracted after experimenting with more than 3200 training images, derived from 99 videos of a pool of 138 patients. The dataset has been provided by a team of expert gastroenterologist surgeons, who have also evaluated the results. The proposed algorithms are tested on a set of more than 1000 selected frame samples from the entire 39 testing videos, to a prevalence of 50% pathologic frames (balanced dataset). The frame elimination of identical and consecutive frames achieved a reduction of 36% of total frames. The best statistical performance for diagnosis of positive pathological frames from a video stream is achieved by utilizing masks in the HSV color model, with sensitivity up to 99%, precision 94.41% to a prevalence of 50%, accuracy up to 96.1%, FNR 1%, FPR 6.8%. The estimated blood-color chart will be clinically validated and used in feature extraction schemes supporting machine learning ML algorithms to improve the localization potential.
基金supported by the National Science and Technology Major Project of China[2017ZX10201302-008]the CAMS Innovation Fund for Medical Sciences[2021-I2M-1-037].
文摘Background:Computer-aided detection(CAD)software has been introduced to automatically interpret digital chest X-rays.This study aimed to evaluate the performance of CAD software(JF CXR-1 v3.0,which was developed by a domestic Hi-tech enterprise)in tuberculosis(TB)case finding in China.Methods:In 2019,we conducted an internal evaluation of the performance of JF CXR-1 v3.0 by reading standard images annotated by a panel of experts.In 2020,using the reading results of chest X-rays by a panel of experts as the reference standard,we conducted an on-site prospective study to evaluate the performance of JF CXR-1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County,Henan Province.Results:Internal assessment results based on 277 standard images showed that JF CXR-1 v3.0 had a sensitivity of 85.94%(95%confidence interval[CI]:77.42%,94.45%)and a specificity of 74.65%(95%CI:68.81%,80.49%)to distinguish active TB from other imaging conditions.In the on-site evaluation phase,images from 3705 outpatients who underwent chest X-ray detection were read by JF CXR-1 v3.0 and local radiologists in parallel.The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89%(95%CI:22.33%,43.46%)and a specificity of 99.28%(95%CI:99.01%,99.56%),while JF CXR-1 v3.0 showed a significantly higher sensitivity of 92.11%(95%CI:86.04%,98.17%)(p<0.05)and maintained high specificity at 94.54%(95%CI:93.81%,95.28%).Conclusions:CAD software could play a positive role in improving the TB case finding capability of township health centers.
文摘Purpose: Surgical templates produced by digital simulation and CAD/CAM allow for three-dimensional control of implant placement. However, due to clinical limitations, there are complications during the use of the template. The purpose of this study was to summarize the complications associated with the use of surgical templates for static computer-aided implant surgery. Methods: Complications were collected during the observation period, and then their implant sites were reanalyzed with simulation software. Results: There were 104 cases during the observation period, 5 cases had complications. Mechanical complications were observed in four cases, including three cases in which the frame of the template fractured during implant placement surgery and one case in which the sleeve fell off the surgical template. In one case, there was an error in the planned position. All cases were mandibular molar cases, and all cases of frame fracture were at the free end defect site. All cases had a Hounsfield unit of more than 700 at the implant site, and some of them had a significantly small jaw opening. Conclusion: Although the spread of CAD/CAM surgical templates has made it possible to avoid problems caused by the position of the implant, it has been difficult to avoid fractures in cases of mandibular free end defects with high Hounsfield unit.
文摘Liver fibrosis is an important pathological precondition for hepatocellular carcinoma.The degree of hepatic fibrosis is positively correlated with liver cancer.Liver fibrosis is a series of pathological and physiological process related to liver cell necrosis and degeneration after chronic liver injury,which finally leads to extracellular matrix and collagen deposition.The early detection and precise staging of fibrosis and cirrhosis are very important for early diagnosis and timely initiation of appropriate therapeutic regimens.The risk of severe liver fibrosis finally progressing to liver carcinoma is＆gt;50%.It is known that biopsy is the gold standard for the diagnosis and staging of liver fibrosis.However,this method has some limitations,such as the potential for pain,sampling variability,and low patient acceptance.Furthermore,the necessity of obtaining a tissue diagnosis of liver fibrosis still remains controversial.An increasing number of reliable non-invasive approaches are now available that are widely applied in clinical practice,mostly in cases of viral hepatitis,resulting in a significantly decreased need for liver biopsy.In fact,the noninvasive detection and evaluation of liver cirrhosis now has good accuracy due to current serum markers,ultrasound imaging,and magnetic resonance imaging quantification techniques.A prominent advantage of the non-invasive detection and assessment of liver fibrosis is that liver fibrosis can be monitored repeatedly and easily in the same patient.Serum biomarkers have the advantages of high applicability（〉95%）and good reproducibility.However,their results can be influenced by different patient conditions because none of these markers are liver-specific.The most promising techniques appear to be transient elastography and magnetic resonance elastography because they provide reliable results for the detection of fibrosis in the advanced stages,and future developments promise to increase the reliability and accuracy of the staging of hepatic fibrosis.This article aims to describe the recent progress in the development of non-invasive assessment methods for the staging of liver fibrosis,with a special emphasize on computer-aided quantitative and deep learning methods.
基金the National Natural Science Foundation of China(No.81371624)the National Key Research and Development Program of China(No.2016YFC0104608)+1 种基金the National Basic Research Program of China(No.2010CB834302)the Shanghai Jiao Tong University Medical Engineering Cross Research Funds(Nos.YG2013MS30 and YG2014ZD05)
文摘Lung cancer is the leading cause of cancer deaths worldwide. Accurate early diagnosis is critical in increasing the 5-year survival rate of lung cancer, so the efficient and accurate detection of lung nodules,the potential precursors to lung cancer, is paramount. In this paper, a computer-aided lung nodule detection system using 3D deep convolutional neural networks(CNNs) is developed. The first multi-scale 11-layer 3D fully convolutional neural network(FCN) is used for screening all lung nodule candidates. Considering relative small sizes of lung nodules and limited memory, the input of the FCN consists of 3D image patches rather than of whole images. The candidates are further classified in the second CNN to get the final result. The proposed method achieves high performance in the LUNA16 challenge and demonstrates the effectiveness of using 3D deep CNNs for lung nodule detection.
文摘Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or precancerous lesions and the capacity to remove them intra-procedurally.Computeraided detection and diagnosis(CAD),thanks to the brand new developed innovations of artificial intelligence,and especially deep-learning techniques,leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy.The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate,and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality.Furthermore,a significant reduction in costs is also expected.In addition,the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule.The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy,as it is reported in literature,addressing evidence,limitations,and future prospects.
基金This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LY17F010003.
文摘Alcoholism is an unhealthy lifestyle associated with alcohol dependence.Not only does drinking for a long time leads to poor mental health and loss of self-control,but alcohol seeps into the bloodstream and shortens the lifespan of the body’s internal organs.Alcoholics often think of alcohol as an everyday drink and see it as a way to reduce stress in their lives because they cannot see the damage in their bodies and they believe it does not affect their physical health.As their drinking increases,they become dependent on alcohol and it affects their daily lives.Therefore,it is important to recognize the dangers of alcohol abuse and to stop drinking as soon as possible.To assist physicians in the diagnosis of patients with alcoholism,we provide a novel alcohol detection system by extracting image features of wavelet energy entropy from magnetic resonance imaging(MRI)combined with a linear regression classifier.Compared with the latest method,the 10-fold cross-validation experiment showed excellent results,including sensitivity 91.54±1.47%,specificity 93.66±1.34%,Precision 93.45±1.27%,accuracy 92.61±0.81%,F1 score 92.48±0.83%and MCC 85.26±1.62%.
文摘The general computer-aided design （CAD） software cannot meet the mould design requirement of the autoclave process for composites, because many parameters such as temperature and pressure should be considered in the mould design process, in addition to the material and geometry of the part. A framed-mould computer-aided design system （FMCAD） used in the autoclave moulding process is proposed in this paper. A function model of the software is presented, in which influence factors such as part structure, mould structure, and process parameters are considered; a design model of the software is established using object oriented （O-O） technology to integrate the stiffness calculation, temperature field calculation, and deformation field calculation of mould in the design, and in the design model, a hybrid model of mould based on calculation feature and form feature is presented to support those calculations. A prototype system is developed, in which a mould design process wizard is built to integrate the input information, calculation, analysis, data storage, display, and design results of mould design. Finally, three design examples are used to verify the prototype.
基金supported by the National Natural Science Foundation of China(81303246)the Jiangsu Provincial Natural Science Foundation of China(BK2011815)+1 种基金the ‘Qing Lan’ Project from Jiangsu Provincial Framework Teacher Support Schemethe Projects of priority-discipline for colleges and universities of Jiangsu Province
文摘AIM: Variation in structure-related components in plant products prompted the trend to establish methods, using multiple or total analog analysis, for their effective quality control. However, the general use of routine quality control is restricted by the limited availability of reference substances. Using an easily available single marker as a reference standard to determine multiple or total analogs should be a practical option. METHOD: In this study, the Ultra-HPLC method was used for the baseline separation of the main components in ginseng extracts. Using a plant chemical component database, ginsenosides in ginseng extracts were identified by Ultra-HPLC-MS analysis. The charged aerosol detection(CAD) system with post-column compensation of the gradient generates a similar response for identical amounts of different analytes, and thus, the content of each ginsenoside in ginseng extracts was determined by comparing the analyte peak area with the reference standard(determination of total analogs by single marker, DTSM). The total ginsenoside content was determined by the summation of reference standard and other ginsenoside components. RESULTS: The results showed that DTSM approaches were available for the determination of total ginsenosides in a high purity ginseng extract because of the removal of impurities. In contrast, DTSM approaches might be suitable for determination of multiple ginsenosides without interference from impurities in the crude ginseng extract. CONCLUSION: Future practical studies similar to the present study should be conducted to verify that DTSM approaches based on CAD with post-column inverse gradient for uniform response are ideal for the quality control of plant products.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R151)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR12).
文摘With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease globally.Breast cancer is the most widely affected cancer worldwide,with an increased death rate percentage.Due to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast cancer.Early detection of breast cancer will reduce the death rate worldwide.The early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive rate.This paper proposed an efficient and optimized deep learning-based feature selection approach with this consideration.This model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm rate.Transfer learning is used in the extraction of features initially.Na ext,a convolution neural network,is used to extract the features.The two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant features.The optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant images.The feature extraction and classification process used two datasets,breast,and MIAS.Compared to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.
基金funded by the Research Fund for Foundation of Hebei University(DXK201914)the President of Hebei University(XZJJ201914)+1 种基金the Post-graduate’s Innovation Fund Project of Hebei University(HBU2022SS003)the Special Project for Cultivating College Students’Scientific and Technological Innovation Ability in Hebei Province(22E50041D).
文摘Due to small size and high occult,metacarpophalangeal fracturediagnosis displays a low accuracy in terms of fracture detection and locationin X-ray images.To efficiently detect metacarpophalangeal fractures on Xrayimages as the second opinion for radiologists,we proposed a novel onestageneural network namedMPFracNet based onRetinaNet.InMPFracNet,a deformable bottleneck block(DBB)was integrated into the bottleneckto better adapt to the geometric variation of the fractures.Furthermore,an integrated feature fusion module(IFFM)was employed to obtain morein-depth semantic and shallow detail features.Specifically,Focal Loss andBalanced L1 Loss were introduced to respectively attenuate the imbalancebetween positive and negative classes and the imbalance between detectionand location tasks.We assessed the proposed model on the test set andachieved an AP of 80.4%for the metacarpophalangeal fracture detection.To estimate the detection performance for fractures with different difficulties,the proposed model was tested on the subsets of metacarpal,phalangeal andtiny fracture test sets and achieved APs of 82.7%,78.5%and 74.9%,respectively.Our proposed framework has state-of-the-art performance for detectingmetacarpophalangeal fractures,which has a strong potential application valuein practical clinical environments.
基金This research has been funded by the Research General Direction at Universidad Santiago de Cali under call No.01-2021.
文摘The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.
