A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs）...A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs） is screened out, thus obtaining ROIs, The strategy consists of three steps： （1） the mammogram is partitioned into a set of non-overlapping blocks with an equal size, and for each block, five statistical features are computed, （2） negative blocks are screened out by the threshold method through rough analyses, （3） the more accurate analysis is done by the cost-sensitive support vector machine to eliminate the block definitely containing no MCCs, Experimental results on real mammograms show that 81.71% of TNRs can be screened out by the proposed method.展开更多
Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying...Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.展开更多
This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculatio...This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculations from a multitude of lines corresponding to the normal fibrous breast tissue.Following this rationale,this paper introduces a novel approach for detecting the speculated lesions in digital mammograms based on multi-scale SIFT(scale-invariant feature transform)orientations.The proposed method starts by estimating a set of key points that best represent the image mammography in a scale space.We then benefit from SIFT algorithm to locally characterize each key point by assigning a consistent orientation.Thereafter,a set of three features are extracted for each pixel in the image mammogram based on these orientations.The extracted features are fed into BDT(binary decision tree)in order to perform per pixel classification and decide whether the pixel is normal or abnormal.We evaluate the proposed system on BCDR(breast cancer digital repository)database and the experimental results show that our method is accurate with 97.95%recognition rate,while it is robust to illumination changes,rotation and scale variations.展开更多
Microcalcification clusters in mammograms are an important early sign of breast cancer. The enhancement of mieroealcifications in mammograms is one of the most important preprocessing techniques for the extraction of ...Microcalcification clusters in mammograms are an important early sign of breast cancer. The enhancement of mieroealcifications in mammograms is one of the most important preprocessing techniques for the extraction of cluster mierocalcifications. In this paper, we present a novel method for the enhancement of microcalcifications. Firstly, the initial microcaleification edges were extracted by using kirsch edge operator, and the diseontinouse edges were linked by employing fi＇aetal teehnique, Then, the continuous closed edges of microcalcifications were filled by using seed filling algorithm. The pixel values of the filled region were replaced by the corresponding pixel values in the original image. Finally, the enhancement of microcalcifications in mammograms was achieved by adding the filled image to the original image. We evaluated the performance of our algorithm by using 50 regions of interesting （ROIs） with microcalcification clusters from DDSM database. The experiment results demonstrate that our CAD system can give better enhancement effect compared with other methods.展开更多
A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of ...A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of the breast.With an appropriate diagnosis,non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant.With the development of the artificial neural network,the deep discriminative model,such as a convolutional neural network,may evaluate the breast lesion to distinguish benign and malignant cancers frommammogram breast masses images.This work accomplished breastmasses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50(ResNet50)model along with an adaptive gradient algorithm,adaptive moment estimation,and stochastic gradient descent optimizers,as well as data augmentations and fine-tuning methods,were implemented.In addition,a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models.The results of training accuracy,p-value,test accuracy,area under the curve,sensitivity,precision,F1-score,specificity,and kappa for adaptive gradient algorithm 25%,75%,100%,and stochastic gradient descent 100%fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.展开更多
Contour is an important pattern descriptor in image processing and particularly in region description, registration and length estimation. In many applications where contour is used, a good segmentation and an efficie...Contour is an important pattern descriptor in image processing and particularly in region description, registration and length estimation. In many applications where contour is used, a good segmentation and an efficient smoothing method are needed. In X-ray images, such as mammograms, where object edge is not clearly discernible, estimating the object’s contour may yield substantial shift along the boundary due to noise or segmentation drawbacks. An appropriate smoothing is therefore required to reduce these effects. In this paper, an approach based on local adaptive threshold segmentation to extract contour and a new smoothing approach founded on Fourier descriptors are introduced. The experimental results of extraction obtained from a set of mammograms and compared with the breast regions delineated by radiologists yielded a percent overlap area of 98.7% ± 0.9% with false positive and negative rates of 0.36 ± 0.74 and 0.93 ± 0.44 respectively. The proposed method was tested on a set of images and improved the accuracy, leading to an average error of less than one pixel.展开更多
Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)les...Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)lesions. However, manual analysis is labor-intensive and prone to diagnostic errors. In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce themorbidity and mortality associated with this carcinoma. In this study, we used a similarity metric-based classi-fication of mammograms using graphical (with two different image sizes) and geometrical approaches (with asingle image size) for comparison to improve the specificity, sensitivity, and accuracy of BC prediction and triageof patients in the order of disease severity. Both classification techniques use two novel algorithms, hereafterreferred to as the normal and hybrid methods, to select representative images from the training sets of healthy andunhealthy groups of mammograms. The normal method identifies a representative image by comparing imageswithin a cohort, whereas the hybrid method adopts a comprehensive approach by comparing images from bothcohorts. This study explored the effects of image size and cardinality of the training set. Finally, we explored theuncharted territory of mapping accuracy versus computational expense for the different approaches adopted inthe current study.展开更多
This paper presents a novel approach for detection of suspicious regions in digitized mammograms. The edges of the suspicious region in mammogram are enhanced using an improved logic filter. The result of further imag...This paper presents a novel approach for detection of suspicious regions in digitized mammograms. The edges of the suspicious region in mammogram are enhanced using an improved logic filter. The result of further image processing gives a gray-level histogram with distinguished characteristics, which facilitates the segmentation of the suspicious masses. The experiment results based on 25 digital sample mammograms, which are definitely diagnosed, are analyzed and evaluated briefly.展开更多
Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However,it is still a very difficult task since the sizes,the shapes...Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However,it is still a very difficult task since the sizes,the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper,we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge,i.e.,continuity and uncertainty. After obtaining a rough boundary,an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.展开更多
Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contr...Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contrast or ambiguous margins. Besides, the shapes and densities of masses in mammograms are various. In this paper, a hybrid method combining a random walks algorithm and Chan-Vese (CV) active contour is proposed for automatic mass segmentation on mammograms. The data set used in this study consists of 1095 mass regions of interest (ROIs). First, the original ROI is preprocessed to suppress noise and surrounding tissues. Based on the preprocessed ROI, a set of seed points is generated for initial random walks segmentation. Afterward, an initial contour of mass and two probability matrices are produced by the initial random walks segmentation. These two probability matrices are used to modify the energy function of the CV model for prevention of contour leaking. Lastly, the final segmentation result is derived by the modified CV model, during which the probability matrices are updated by inserting several rounds of random walks. The proposed method is tested and compared with other four methods. The segmentation results are evaluated based on four evaluation metrics. Experimental results indicate that the proposed method produces more accurate mass segmentation results than the other four methods.展开更多
Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research l...Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors.In addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram.Accordingly,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of mammograms.For this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS databases.Maximumclassification accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing models.Furthermore,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.展开更多
Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep infor...Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.展开更多
Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use ...Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.展开更多
Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast can...Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast cancer are needed.The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy.Machine learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many patients.There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising results.The proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia.This paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a classifier.The NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,K-Nearest Neighbor(KNN),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to SVM.Adaboost has produced a fairly well accuracy of 82%,KNN has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.展开更多
Feature selection（FS） refers to the process of selecting those input attributes that are most predictive of a given outcome. Unlike other dimensionality reduction methods,feature selectors preserve the original mean...Feature selection（FS） refers to the process of selecting those input attributes that are most predictive of a given outcome. Unlike other dimensionality reduction methods,feature selectors preserve the original meaning of the features after reduction. The benefits of FS are twofold：it considerably decreases the running time of the induction algorithm,and increases the accuracy of the resulting model. This paper analyses the FS process in mammogram classification using fuzzy logic and rough set theory. Rough set and fuzzy logic based Quickreduct algorithms are applied for the FS from the features extracted using gray level co-occurence matrix（GLCM） constructed over the mammogram region. The predictive accuracy of the features is tested using NaiveBayes,Ripper,C4.5,and ant-miner algorithms. The results show that the ant-miner produces significant result comparing with others and the number of features selected using fuzzy-rough quick reduct algorithm is minimum,too.展开更多
Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence o...Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare data.In such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data expansively.Furthermore,breast cancer(BC)has been found to be a major cause of mortality among ladies globally.Earlier detection and classification of BC using digital mammograms can decrease the mortality rate.This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT environment.The goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC.The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms.In addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms.Moreover,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented technique.To enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques.The DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.展开更多
Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer.The present research work is useful in image processing for characterizing shape and gray-scale complexity....Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer.The present research work is useful in image processing for characterizing shape and gray-scale complexity.The proposed Modified Differential Box Counting(MDBC)extract Fractal features such as Fractal Dimension(FD),Lacunarity,and Succolarity for shape characterization.In traditional DBC method,the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels.The problem is overcome by the proposedMDBCmethod that uses box over counting and under counting that covers the whole image with required scale.In MDBC method,the suitable box size selection and Under Counting Shifting rule computation handles over counting problem.An advantage of the model is that the proposed MDBC work with recently developed methods showed that our method outperforms automatic detection and classification.The extracted features are fed to K-Nearest Neighbour(KNN)and Support Vector Machine(SVM)categorizes the mammograms into normal,benign,and malignant.The method is tested on mini MIAS datasets yields good results with improved accuracy of 93%,whereas the existing FD,GLCM,Texture and Shape feature method has 91%accuracy.展开更多
Vascular calcification(VC) is common among patientswith chronic kidney disease(CKD).The severity of VC is associated with increased risk of cardiovascular events and mortality.Risk factors for VC include traditional c...Vascular calcification(VC) is common among patientswith chronic kidney disease(CKD).The severity of VC is associated with increased risk of cardiovascular events and mortality.Risk factors for VC include traditional cardiovascular risk factors as well as CKD-related risk factors such as increased calcium and phosphate load.VC is observed in arteries of all sizes from small arterioles to aorta,both in the intima and the media of arterial wall.Several imaging techniques have been utilized in the evaluation of the extent and the severity of VC.Plain radiographs are simple and readily available but with the limitation of decreased sensitivity and subjective and semi-quantitative quantification methods.Mammography,especially useful among women,offers a unique way to study breast arterial calcification,which is largely a medial-type calcification.Ultrasonography is suitable for calcification in superficial arteries.Analyses of wall thickness and lumen size are also possible.Computed tomography(CT) scan,the gold standard,is the most sensitive technique for evaluation of VC.CT scan of coronary artery calcification is not only useful for cardiovascular risk stratification but also offers an accurate and an objective analysis of the severity and progression.展开更多
MRI is an excellent option for detection of breast cancer for some selected groups, including those patients with a high probability to hit the disease. However, the high costs and low availability of the device have ...MRI is an excellent option for detection of breast cancer for some selected groups, including those patients with a high probability to hit the disease. However, the high costs and low availability of the device have led to a decline in the application of imaging MRI. The aim of this study was to review usefulness of MRI as a new complementary way to detect breast cancer in routine annual checkup for women breasts of certain ages and breast mass. A cross-sectional Descriptive MRI study was performed on 105 asymptomatic women with a mean age of 49 years. The study group with at least one risk factor of breast cancer were presenting for routine annual screening or follow up at King Abdulaziz University Hospital in Jeddah. It has been found that, 48 patients had biopsy, they were recommended by magnetic resonance imaging and only 14 had positive results, while magnetic resonance imaging suggested 16 and mammography had 62 positive results. Magnetic resonance imaging is not recommended for the average-risk or the general population either;it had been advised for screening the high-risk women of breast cancer. Sensitivity of magnetic resonance imaging has been found to be much higher than of mammography but specificity was generally lower. We propose that it is reasonable to consider MRI as a complement to mammography in screening patients who were at high risk for breast cancer because Magnetic Resonance Imaging can detect small foci that are occult in mammography but we don’t advise to check with the general population.展开更多
Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent years.Early diagnosis is essential to handle breast cancer patients for treatment at the right time.Screenin...Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent years.Early diagnosis is essential to handle breast cancer patients for treatment at the right time.Screening with mammography is the preferred examination for breast cancer,as it is available worldwide and inexpensive.Computer-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,early.The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations.Processing of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal classes.This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses.The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement technique.The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast metrics.The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives.The segmentation process is performed using Otsu’smultiple thresholding method.This method segments the image regions into five classes with variable intensities using four thresholds.Its effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of masses.The performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)metrics.The segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an expert.Hence,the proposed framework achieves promising results for aiding radiologists in screening of mammograms,accurately.展开更多
文摘A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs） is screened out, thus obtaining ROIs, The strategy consists of three steps： （1） the mammogram is partitioned into a set of non-overlapping blocks with an equal size, and for each block, five statistical features are computed, （2） negative blocks are screened out by the threshold method through rough analyses, （3） the more accurate analysis is done by the cost-sensitive support vector machine to eliminate the block definitely containing no MCCs, Experimental results on real mammograms show that 81.71% of TNRs can be screened out by the proposed method.
基金This project was supported by the Deanship of Scientific Research at Prince SattamBin Abdulaziz University under research Project#(PSAU-2022/01/20287).
文摘Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.
文摘This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculations from a multitude of lines corresponding to the normal fibrous breast tissue.Following this rationale,this paper introduces a novel approach for detecting the speculated lesions in digital mammograms based on multi-scale SIFT(scale-invariant feature transform)orientations.The proposed method starts by estimating a set of key points that best represent the image mammography in a scale space.We then benefit from SIFT algorithm to locally characterize each key point by assigning a consistent orientation.Thereafter,a set of three features are extracted for each pixel in the image mammogram based on these orientations.The extracted features are fed into BDT(binary decision tree)in order to perform per pixel classification and decide whether the pixel is normal or abnormal.We evaluate the proposed system on BCDR(breast cancer digital repository)database and the experimental results show that our method is accurate with 97.95%recognition rate,while it is robust to illumination changes,rotation and scale variations.
基金National Natural Science Foundation of China grant number: 30971019
文摘Microcalcification clusters in mammograms are an important early sign of breast cancer. The enhancement of mieroealcifications in mammograms is one of the most important preprocessing techniques for the extraction of cluster mierocalcifications. In this paper, we present a novel method for the enhancement of microcalcifications. Firstly, the initial microcaleification edges were extracted by using kirsch edge operator, and the diseontinouse edges were linked by employing fi＇aetal teehnique, Then, the continuous closed edges of microcalcifications were filled by using seed filling algorithm. The pixel values of the filled region were replaced by the corresponding pixel values in the original image. Finally, the enhancement of microcalcifications in mammograms was achieved by adding the filled image to the original image. We evaluated the performance of our algorithm by using 50 regions of interesting （ROIs） with microcalcification clusters from DDSM database. The experiment results demonstrate that our CAD system can give better enhancement effect compared with other methods.
基金This research was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)[NRF-2019R1F1A1062397,NRF-2021R1F1A1059665]Brain Korea 21 FOUR Project(Dept.of IT Convergence Engineering,Kumoh National Institute of Technology)This paper was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)[P0017123,The Competency Development Program for Industry Specialist].
文摘A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of the breast.With an appropriate diagnosis,non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant.With the development of the artificial neural network,the deep discriminative model,such as a convolutional neural network,may evaluate the breast lesion to distinguish benign and malignant cancers frommammogram breast masses images.This work accomplished breastmasses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50(ResNet50)model along with an adaptive gradient algorithm,adaptive moment estimation,and stochastic gradient descent optimizers,as well as data augmentations and fine-tuning methods,were implemented.In addition,a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models.The results of training accuracy,p-value,test accuracy,area under the curve,sensitivity,precision,F1-score,specificity,and kappa for adaptive gradient algorithm 25%,75%,100%,and stochastic gradient descent 100%fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.
文摘Contour is an important pattern descriptor in image processing and particularly in region description, registration and length estimation. In many applications where contour is used, a good segmentation and an efficient smoothing method are needed. In X-ray images, such as mammograms, where object edge is not clearly discernible, estimating the object’s contour may yield substantial shift along the boundary due to noise or segmentation drawbacks. An appropriate smoothing is therefore required to reduce these effects. In this paper, an approach based on local adaptive threshold segmentation to extract contour and a new smoothing approach founded on Fourier descriptors are introduced. The experimental results of extraction obtained from a set of mammograms and compared with the breast regions delineated by radiologists yielded a percent overlap area of 98.7% ± 0.9% with false positive and negative rates of 0.36 ± 0.74 and 0.93 ± 0.44 respectively. The proposed method was tested on a set of images and improved the accuracy, leading to an average error of less than one pixel.
基金supported by the Commissioned Research through SRM University-AP,India,Research Grant–Central Facility under Grant No.SRMAP/URG/CF/2023-24/045.
文摘Breast carcinoma is the second most common cause of cancer-related deaths. Radiologists often use mammog-raphy, a noninvasive and inexpensive imaging tool, for the detection and classification of breast cancer (BC)lesions. However, manual analysis is labor-intensive and prone to diagnostic errors. In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce themorbidity and mortality associated with this carcinoma. In this study, we used a similarity metric-based classi-fication of mammograms using graphical (with two different image sizes) and geometrical approaches (with asingle image size) for comparison to improve the specificity, sensitivity, and accuracy of BC prediction and triageof patients in the order of disease severity. Both classification techniques use two novel algorithms, hereafterreferred to as the normal and hybrid methods, to select representative images from the training sets of healthy andunhealthy groups of mammograms. The normal method identifies a representative image by comparing imageswithin a cohort, whereas the hybrid method adopts a comprehensive approach by comparing images from bothcohorts. This study explored the effects of image size and cardinality of the training set. Finally, we explored theuncharted territory of mapping accuracy versus computational expense for the different approaches adopted inthe current study.
基金This research is partly supported by the National Natural Science Foundation of China! (No.69873031).
文摘This paper presents a novel approach for detection of suspicious regions in digitized mammograms. The edges of the suspicious region in mammogram are enhanced using an improved logic filter. The result of further image processing gives a gray-level histogram with distinguished characteristics, which facilitates the segmentation of the suspicious masses. The experiment results based on 25 digital sample mammograms, which are definitely diagnosed, are analyzed and evaluated briefly.
基金Project (No. 60505009) supported by the National Natural Science Foundation of China
文摘Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However,it is still a very difficult task since the sizes,the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper,we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge,i.e.,continuity and uncertainty. After obtaining a rough boundary,an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.
基金Project (Nos. 60772092 and 81101903) supported by the National Natural Science Foundation of China
文摘Accurate mass segmentation on mammograms is a critical step in computer-aided diagnosis (CAD) systems. It is also a challenging task since some of the mass lesions are embedded in normal tissues and possess poor contrast or ambiguous margins. Besides, the shapes and densities of masses in mammograms are various. In this paper, a hybrid method combining a random walks algorithm and Chan-Vese (CV) active contour is proposed for automatic mass segmentation on mammograms. The data set used in this study consists of 1095 mass regions of interest (ROIs). First, the original ROI is preprocessed to suppress noise and surrounding tissues. Based on the preprocessed ROI, a set of seed points is generated for initial random walks segmentation. Afterward, an initial contour of mass and two probability matrices are produced by the initial random walks segmentation. These two probability matrices are used to modify the energy function of the CV model for prevention of contour leaking. Lastly, the final segmentation result is derived by the modified CV model, during which the probability matrices are updated by inserting several rounds of random walks. The proposed method is tested and compared with other four methods. The segmentation results are evaluated based on four evaluation metrics. Experimental results indicate that the proposed method produces more accurate mass segmentation results than the other four methods.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors.In addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram.Accordingly,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of mammograms.For this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS databases.Maximumclassification accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing models.Furthermore,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.
文摘Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.
文摘Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation—Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project grant code(NU/IFC/ENT/01/009)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Since reporting cases of breast cancer are on the rise all over the world.Especially in regions such as Pakistan,Saudi Arabia,and the United States.Efficient methods for the early detection and diagnosis of breast cancer are needed.The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy.Machine learning based practices has increased the accuracy and efficiency of medical diagnosis,which has helped save lives of many patients.There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images(MRIs),mammograms,X-rays,ultrasounds,and histopathological images,but magnetic resonance(MR)and mammogram imaging have proved to present the promising results.The proposed paper has presented the results of classification algorithms over Breast Cancer(BC)mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia.This paper has developed a novel approach called the novel spectral extraction algorithm(NSEA)that uses feature extraction and fusion by using local binary pattern(LBP)and bilateral algorithms,as well as a support vector machine(SVM)as a classifier.The NSEA with the SVM classifier demonstrated a promising accuracy of 94%and an elapsed time of 0.68 milliseconds,which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes,logistic regression,K-Nearest Neighbor(KNN),Gaussian Discriminant Analysis(GDA),AdaBoost and Extreme Learning Machine(ELM).ELM produced the comparative accuracy of 94%however has a lower elapsed time of 1.35 as compared to SVM.Adaboost has produced a fairly well accuracy of 82%,KNN has a low accuracy of 66%.However Logistic Regression,GDA and Naïve Bayes have produced the lowest accuracies of 47%,43%and 42%.
文摘Feature selection（FS） refers to the process of selecting those input attributes that are most predictive of a given outcome. Unlike other dimensionality reduction methods,feature selectors preserve the original meaning of the features after reduction. The benefits of FS are twofold：it considerably decreases the running time of the induction algorithm,and increases the accuracy of the resulting model. This paper analyses the FS process in mammogram classification using fuzzy logic and rough set theory. Rough set and fuzzy logic based Quickreduct algorithms are applied for the FS from the features extracted using gray level co-occurence matrix（GLCM） constructed over the mammogram region. The predictive accuracy of the features is tested using NaiveBayes,Ripper,C4.5,and ant-miner algorithms. The results show that the ant-miner produces significant result comparing with others and the number of features selected using fuzzy-rough quick reduct algorithm is minimum,too.
基金We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number(TURSP-2020/328),Taif University,Taif,Saudi Arabia.
文摘Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare data.In such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data expansively.Furthermore,breast cancer(BC)has been found to be a major cause of mortality among ladies globally.Earlier detection and classification of BC using digital mammograms can decrease the mortality rate.This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT environment.The goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC.The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms.In addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms.Moreover,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented technique.To enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques.The DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.
文摘Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer.The present research work is useful in image processing for characterizing shape and gray-scale complexity.The proposed Modified Differential Box Counting(MDBC)extract Fractal features such as Fractal Dimension(FD),Lacunarity,and Succolarity for shape characterization.In traditional DBC method,the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels.The problem is overcome by the proposedMDBCmethod that uses box over counting and under counting that covers the whole image with required scale.In MDBC method,the suitable box size selection and Under Counting Shifting rule computation handles over counting problem.An advantage of the model is that the proposed MDBC work with recently developed methods showed that our method outperforms automatic detection and classification.The extracted features are fed to K-Nearest Neighbour(KNN)and Support Vector Machine(SVM)categorizes the mammograms into normal,benign,and malignant.The method is tested on mini MIAS datasets yields good results with improved accuracy of 93%,whereas the existing FD,GLCM,Texture and Shape feature method has 91%accuracy.
文摘Vascular calcification(VC) is common among patientswith chronic kidney disease(CKD).The severity of VC is associated with increased risk of cardiovascular events and mortality.Risk factors for VC include traditional cardiovascular risk factors as well as CKD-related risk factors such as increased calcium and phosphate load.VC is observed in arteries of all sizes from small arterioles to aorta,both in the intima and the media of arterial wall.Several imaging techniques have been utilized in the evaluation of the extent and the severity of VC.Plain radiographs are simple and readily available but with the limitation of decreased sensitivity and subjective and semi-quantitative quantification methods.Mammography,especially useful among women,offers a unique way to study breast arterial calcification,which is largely a medial-type calcification.Ultrasonography is suitable for calcification in superficial arteries.Analyses of wall thickness and lumen size are also possible.Computed tomography(CT) scan,the gold standard,is the most sensitive technique for evaluation of VC.CT scan of coronary artery calcification is not only useful for cardiovascular risk stratification but also offers an accurate and an objective analysis of the severity and progression.
文摘MRI is an excellent option for detection of breast cancer for some selected groups, including those patients with a high probability to hit the disease. However, the high costs and low availability of the device have led to a decline in the application of imaging MRI. The aim of this study was to review usefulness of MRI as a new complementary way to detect breast cancer in routine annual checkup for women breasts of certain ages and breast mass. A cross-sectional Descriptive MRI study was performed on 105 asymptomatic women with a mean age of 49 years. The study group with at least one risk factor of breast cancer were presenting for routine annual screening or follow up at King Abdulaziz University Hospital in Jeddah. It has been found that, 48 patients had biopsy, they were recommended by magnetic resonance imaging and only 14 had positive results, while magnetic resonance imaging suggested 16 and mammography had 62 positive results. Magnetic resonance imaging is not recommended for the average-risk or the general population either;it had been advised for screening the high-risk women of breast cancer. Sensitivity of magnetic resonance imaging has been found to be much higher than of mammography but specificity was generally lower. We propose that it is reasonable to consider MRI as a complement to mammography in screening patients who were at high risk for breast cancer because Magnetic Resonance Imaging can detect small foci that are occult in mammography but we don’t advise to check with the general population.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Breast cancer is themost common type of cancer,and it is the reason for cancer death toll in women in recent years.Early diagnosis is essential to handle breast cancer patients for treatment at the right time.Screening with mammography is the preferred examination for breast cancer,as it is available worldwide and inexpensive.Computer-Aided Detection(CAD)systems are used to analyze medical images to detect breast cancer,early.The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations.Processing of mammogram images has four main steps:pre-processing,segmentation of the region of interest,feature extraction and classification of the images into normal or abnormal classes.This paper presents an efficient framework for processing of mammogram images and introduces an algorithm for segmentation of the images to detect masses.The pre-processing step of mammogram images includes removal of digitization noise using a 2D median filter,removal of artifacts using morphological operations,and contrast enhancement using a fuzzy enhancement technique.The proposed fuzzy image enhancement technique is analyzed and compared with conventional techniques based on an Enhancement Measure(EME)and local contrast metrics.The comparison shows an outstanding performance of the proposed technique from the visual and numerical perspectives.The segmentation process is performed using Otsu’smultiple thresholding method.This method segments the image regions into five classes with variable intensities using four thresholds.Its effectiveness is measured based on visual quality of the segmentation output,as it gives details about the image and positions of masses.The performance of the proposed framework is measured using Dice coefficient,Hausdorff,and Peak Signal-to-Noise Ratio(PSNR)metrics.The segmented tumor region with the proposed segmentation method is 81%of the ground truth region provided by an expert.Hence,the proposed framework achieves promising results for aiding radiologists in screening of mammograms,accurately.
