Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Aug...Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.展开更多
The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)loss.However,when the label presents an intrinsic ordinal...The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)loss.However,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better generalization.To improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.The merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)module.First,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree.Hence,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction variance.Second,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature perturbation.During training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix.Experimental results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.展开更多
Gliomas have the highest mortality rate of all brain tumors.Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’survival rates.This paper proposes a hier...Gliomas have the highest mortality rate of all brain tumors.Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’survival rates.This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network(HMAC-Net),which effectively combines global features and local features.The network framework consists of three parallel layers:The global feature extraction layer,the local feature extraction layer,and the multi-scale feature fusion layer.A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy.In the local feature extraction layer,a bilateral local attention mechanism is introduced to improve the extraction of relevant information between adjacent slices.In the multi-scale feature fusion layer,a channel fusion block combining convolutional attention mechanism and residual inverse multi-layer perceptron is proposed to prevent gradient disappearance and network degradation and improve feature representation capability.The double-branch iterative multi-scale classification block is used to improve the classification performance.On the brain glioma risk grading dataset,the results of the ablation experiment and comparison experiment show that the proposed HMAC-Net has the best performance in both qualitative analysis of heat maps and quantitative analysis of evaluation indicators.On the dataset of skin cancer classification,the generalization experiment results show that the proposed HMAC-Net has a good generalization effect.展开更多
Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to ...Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).展开更多
Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of ar...Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification models.At the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality problem.For resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)technique.The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results.Primarily,the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity.In addition,the information gain(IG)approach is applied for the optimal selection of feature subsets.Also,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm.The choice of IG and GSO approaches results in promising medical data classification results.The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets.The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.展开更多
Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform...Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform the effective classification task.With this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data classification.The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models.Besides,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance.Moreover,the OSVM model investigates the clustered medical data to perform classification process.Furthermore,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM technique.Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.展开更多
With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT dev...With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.展开更多
Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this art...Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.展开更多
Objective:To explore the diagnostic value of traditional Chinese medical(TCM)dialectical classification in Hashimoto's thyroiditis complicated with suspicious nodules.Methods:The clinical data of patients with Has...Objective:To explore the diagnostic value of traditional Chinese medical(TCM)dialectical classification in Hashimoto's thyroiditis complicated with suspicious nodules.Methods:The clinical data of patients with Hashimoto's thyroiditis complicated with thyroid nodules in the Department of Breast and thyroid surgery of Weifang Hospital of traditional Chinese Medicine from January 2018 to December 2019 were collected.The patients were examined by 2 or more experienced TCM doctors,and the four diagnostic data were obtained,and then the relevant syndrome types of the patients were judged according to the data.According to the color Doppler ultrasonographic features of thyroid nodules,the patients who met the indication of fine needle aspiration biopsy of thyroid nodules were selected and underwent fine needle aspiration biopsy of thyroid nodules before operation.To analyze the clinical diagnostic value of that the ultrasonic mode used in this study and thyroid cytopathology Bethesda report system combine dialectical classification of traditional Chinese medicine in Hashimoto's thyroiditis complicated with suspected thyroid nodules.Result:A total of 89 patients with Hashimoto's thyroiditis complicated with thyroid nodules were collected.according to the ultrasonic mode,the difference between different modes was statistically significant(P<0.05).The mode of color ultrasound is also related to the dialectical classification of traditional Chinese medicine.The patients with high malignant risk score are mainly qi depression and phlegm stagnation,phlegm and blood stasis,while those with low score are exuberant liver fire and heart liver yin deficiency.According to the study of different The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)classification,the dialectical classification of patients with higher TBSRTC classification was more inclined to qi depression and phlegm stagnation,phlegm and blood stasis,and there was significant difference between different classification(P<0.05).Conclusion:Qi depression and phlegm obstruction,phlegm and blood stasis have high ultrasound malignant risk score and high TBSRTC classification grade in patients with Hashimoto's thyroiditis complicated with suspected thyroid nodules,which has important clinical diagnostic value.展开更多
We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract informa...We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract information from medical text,facilitating more accurate classification while minimizing the number of trainable parameters.Extensive experiments conducted on various datasets demonstrate the effectiveness of our approach.展开更多
The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer g...The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.展开更多
Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine.The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery...Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine.The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery.Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells.In medical practices,histopathological investigation of tissue specimens generally takes place in a conventional way,whereas automated tools that use Artificial Intelligence(AI)techniques can produce effective results in disease detection performance.In this background,the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification(AAI-CCDC)technique.The proposed AAICCDC technique focuses on the examination of histopathological images to diagnose colorectal cancer.Initially,AAI-CCDC technique performs preprocessing in three levels such as gray scale transformation,Median Filtering(MF)-based noise removal,and contrast improvement.In addition,Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature vectors.Furthermore,Glowworm Swarm Optimization(GSO)with Stacked Gated Recurrent Unit(SGRU)model is used for the detection and classification of colorectal cancer.The proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches.展开更多
This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provi...This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds.So,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data prediction.In this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical data.The output of optimization is either 0 or 1 i.e.,odd or even in QC.From this output value,the data is identified according to the class.Meanwhile,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter method.After the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by LTSA.The motivation of the minimal objective is to remain faster.However,some computations can be performed more efficiently by the proposed model.In testing,the test data was evaluated by minimal loss function.The outcomes were assessed in terms of accuracy,computational time,and so on.For this,databases like Lymphography,Dermatology,and Arrhythmia were used.展开更多
The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning mode...The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification.Initially,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation.Among these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 score.As a result,EfficientNetwas selected as the foundation for implementing Dynamic GradNet.Dynamic GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia detection.These adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and intervention.The model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD progression.The proposed model outperformed the baseline architectures,achieving remarkable generalizability across all stages.This findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification metrics.These findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based models.The model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early detection of neurodegenerative diseases.展开更多
Medical image classification models often lack validation across diverse datasets,limiting their generalization in clinical settings.This study evaluates and optimizes an enhanced DenseNet-121 model,integrating dilate...Medical image classification models often lack validation across diverse datasets,limiting their generalization in clinical settings.This study evaluates and optimizes an enhanced DenseNet-121 model,integrating dilated convolutions and Squeeze-and-Excitation(SE)blocks,for multi-modal medical image classification.We assess its robustness across Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and histopathology datasets,focusing on cross-domain and crossmodality performance.Experiments reveal strong in-domain results but significant degradation in cross-modality tasks(e.g.,MRI-to-CT accuracy drops to~0.5).To address this,we propose two strategies:(1)multi-modal joint training,which boosts cross-modality accuracy to 0.87,and(2)Cycle-Consistent Adversarial Networks(CycleGAN)-based modality translation,improving performance to 0.7.Gradient-weighted Class Activation Mapping(Grad-CAM)visualizations confirm the model’s focus on clinically relevant regions,enhancing interpretability.Findings highlight the superiority of multi-modal training while demonstrating CycleGAN’s utility when target-domain data is scarce.Future work should explore larger multi-center datasets and advanced domain adaptation to further improve robustness.展开更多
Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective intervention.While Deep Learning(DL)approaches have shown p...Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective intervention.While Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class imbalance.This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature selection.The study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and older.The proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD classification.The ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.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
The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence...The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of prepro- cessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations con- sidered include the discretization of numeric attributes, the selection of attribute subset（s）, and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.展开更多
Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung...Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening.However,LDCT images suffer from severe noise,largely influencing the performance of lung nodule classification.Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT(NDCT)images as the supervision for the denoising task,which is impractical in the context of clinical diagnosis using LDCT.To jointly train these two tasks in a unified framework without the NDCT images,this paper introduces a novel self-supervised method,termed strided Noise2Neighbors or SN2N,for blind medical image denoising and lung nodule classification,where the supervision is generated from noisy input images.More specifically,the proposed SN2N can construct the supervision infor-mation from its neighbors for LDCT denoising,which does not need NDCT images anymore.The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification.Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision.Moreover,our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.展开更多
文摘Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.
基金This work was supported in part by the Natural Science Foundation of Shanghai(21ZR1403600)the National Natural Science Foundation of China(62176059)+3 种基金Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)Zhang Jiang Laboratory,Shanghai Sailing Program(21YF1402800)Shanghai Municipal of Science and Technology Project(20JC1419500)Shanghai Center for Brain Science and Brain-inspired Technology.
文摘The performance of medical image classification has been enhanced by deep convolutional neural networks(CNNs),which are typically trained with cross-entropy(CE)loss.However,when the label presents an intrinsic ordinal property in nature,e.g.,the development from benign to malignant tumor,CE loss cannot take into account such ordinal information to allow for better generalization.To improve model generalization with ordinal information,we propose a novel meta ordinal regression forest(MORF)method for medical image classification with ordinal labels,which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.The merits of the proposed MORF come from the following two components:A tree-wise weighting net(TWW-Net)and a grouped feature selection(GFS)module.First,the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree.Hence,all the trees possess varying weights,which is helpful for alleviating the tree-wise prediction variance.Second,the GFS module enables a dynamic forest rather than a fixed one that was previously used,allowing for random feature perturbation.During training,we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix.Experimental results on two medical image classification datasets with ordinal labels,i.e.,LIDC-IDRI and Breast Ultrasound datasets,demonstrate the superior performances of our MORF method over existing state-of-the-art methods.
基金Major Program of National Natural Science Foundation of China(NSFC12292980,NSFC12292984)National Key R&D Program of China(2023YFA1009000,2023YFA1009004,2020YFA0712203,2020YFA0712201)+2 种基金Major Program of National Natural Science Foundation of China(NSFC12031016)Beijing Natural Science Foundation(BNSFZ210003)Department of Science,Technology and Information of the Ministry of Education(8091B042240).
文摘Gliomas have the highest mortality rate of all brain tumors.Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’survival rates.This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network(HMAC-Net),which effectively combines global features and local features.The network framework consists of three parallel layers:The global feature extraction layer,the local feature extraction layer,and the multi-scale feature fusion layer.A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy.In the local feature extraction layer,a bilateral local attention mechanism is introduced to improve the extraction of relevant information between adjacent slices.In the multi-scale feature fusion layer,a channel fusion block combining convolutional attention mechanism and residual inverse multi-layer perceptron is proposed to prevent gradient disappearance and network degradation and improve feature representation capability.The double-branch iterative multi-scale classification block is used to improve the classification performance.On the brain glioma risk grading dataset,the results of the ablation experiment and comparison experiment show that the proposed HMAC-Net has the best performance in both qualitative analysis of heat maps and quantitative analysis of evaluation indicators.On the dataset of skin cancer classification,the generalization experiment results show that the proposed HMAC-Net has a good generalization effect.
基金a grant from the“Research Center of the Female Scientific and Medical Colleges”,the Deanship of Scientific Research,King Saud University.
文摘Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/180/43)Taif University Researchers Supporting Project number(TURSP-2020/346)Taif University,Taif,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:22UQU4340237DSR02.
文摘Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification models.At the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality problem.For resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)technique.The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results.Primarily,the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity.In addition,the information gain(IG)approach is applied for the optimal selection of feature subsets.Also,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm.The choice of IG and GSO approaches results in promising medical data classification results.The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets.The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.
基金This project was supported financially by Institution Fund projects under Grant No.(IFPIP-249-145-1442).
文摘Medical data classification becomes a hot research topic in the healthcare sector to aid physicians in the healthcare sector for decision making.Besides,the advances of machine learning(ML)techniques assist to perform the effective classification task.With this motivation,this paper presents a Fuzzy Clustering Approach Based on Breadth-first Search Algorithm(FCA-BFS)with optimal support vector machine(OSVM)model,named FCABFS-OSVM for medical data classification.The proposed FCABFS-OSVM technique intends to classify the healthcare data by the use of clustering and classification models.Besides,the proposed FCABFSOSVM technique involves the design of FCABFS technique to cluster the medical data which helps to boost the classification performance.Moreover,the OSVM model investigates the clustered medical data to perform classification process.Furthermore,Archimedes optimization algorithm(AOA)is utilized to the SVM parameters and boost the medical data classification results.A wide range of simulations takes place to highlight the promising performance of the FCABFS-OSVM technique.Extensive comparison studies reported the enhanced outcomes of the FCABFS-OSVM technique over the recent state of art approaches.
文摘With the exponential developments of wireless networking and inexpensive Internet of Things(IoT),a wide range of applications has been designed to attain enhanced services.Due to the limited energy capacity of IoT devices,energy-aware clustering techniques can be highly preferable.At the same time,artificial intelligence(AI)techniques can be applied to perform appropriate disease diagnostic processes.With this motivation,this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification(SSAC-MDC)model in an IoT environment.The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment.The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering(SSAC)technique to choose the proper set of cluster heads(CHs)and construct clusters.Besides,the medical data classification process involves three different subprocesses namely pre-processing,autoencoder(AE)based classification,and improved beetle antenna search(IBAS)based parameter tuning.The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work.For show-casing the improved performance of the SSAC-MDC technique,a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods.
文摘Medical image classification has played an important role in the medical field, and the related method based on deep learning has become an important and powerful technique in medical image classification. In this article, we propose a simplified inception module based Hadamard attention (SI + HA) mechanism for medical image classification. Specifically, we propose a new attention mechanism: Hadamard attention mechanism. It improves the accuracy of medical image classification without greatly increasing the complexity of the model. Meanwhile, we adopt a simplified inception module to improve the utilization of parameters. We use two medical image datasets to prove the superiority of our proposed method. In the BreakHis dataset, the AUCs of our method can reach 98.74%, 98.38%, 98.61% and 97.67% under the magnification factors of 40×, 100×, 200× and 400×, respectively. The accuracies can reach 95.67%, 94.17%, 94.53% and 94.12% under the magnification factors of 40×, 100×, 200× and 400×, respectively. In the KIMIA Path 960 dataset, the AUCs and accuracy of our method can reach 99.91% and 99.03%. It is superior to the currently popular methods and can significantly improve the effectiveness of medical image classification.
文摘Objective:To explore the diagnostic value of traditional Chinese medical(TCM)dialectical classification in Hashimoto's thyroiditis complicated with suspicious nodules.Methods:The clinical data of patients with Hashimoto's thyroiditis complicated with thyroid nodules in the Department of Breast and thyroid surgery of Weifang Hospital of traditional Chinese Medicine from January 2018 to December 2019 were collected.The patients were examined by 2 or more experienced TCM doctors,and the four diagnostic data were obtained,and then the relevant syndrome types of the patients were judged according to the data.According to the color Doppler ultrasonographic features of thyroid nodules,the patients who met the indication of fine needle aspiration biopsy of thyroid nodules were selected and underwent fine needle aspiration biopsy of thyroid nodules before operation.To analyze the clinical diagnostic value of that the ultrasonic mode used in this study and thyroid cytopathology Bethesda report system combine dialectical classification of traditional Chinese medicine in Hashimoto's thyroiditis complicated with suspected thyroid nodules.Result:A total of 89 patients with Hashimoto's thyroiditis complicated with thyroid nodules were collected.according to the ultrasonic mode,the difference between different modes was statistically significant(P<0.05).The mode of color ultrasound is also related to the dialectical classification of traditional Chinese medicine.The patients with high malignant risk score are mainly qi depression and phlegm stagnation,phlegm and blood stasis,while those with low score are exuberant liver fire and heart liver yin deficiency.According to the study of different The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)classification,the dialectical classification of patients with higher TBSRTC classification was more inclined to qi depression and phlegm stagnation,phlegm and blood stasis,and there was significant difference between different classification(P<0.05).Conclusion:Qi depression and phlegm obstruction,phlegm and blood stasis have high ultrasound malignant risk score and high TBSRTC classification grade in patients with Hashimoto's thyroiditis complicated with suspected thyroid nodules,which has important clinical diagnostic value.
文摘We present an approach to classify medical text at a sentence level automatically.Given the inherent complexity of medical text classification,we employ adapters based on pre-trained language models to extract information from medical text,facilitating more accurate classification while minimizing the number of trainable parameters.Extensive experiments conducted on various datasets demonstrate the effectiveness of our approach.
文摘The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.More specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.
基金This work was funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-398–247–1443).
文摘Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine.The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery.Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells.In medical practices,histopathological investigation of tissue specimens generally takes place in a conventional way,whereas automated tools that use Artificial Intelligence(AI)techniques can produce effective results in disease detection performance.In this background,the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification(AAI-CCDC)technique.The proposed AAICCDC technique focuses on the examination of histopathological images to diagnose colorectal cancer.Initially,AAI-CCDC technique performs preprocessing in three levels such as gray scale transformation,Median Filtering(MF)-based noise removal,and contrast improvement.In addition,Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature vectors.Furthermore,Glowworm Swarm Optimization(GSO)with Stacked Gated Recurrent Unit(SGRU)model is used for the detection and classification of colorectal cancer.The proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches.
基金This research work was funded by Institutional fund projects under Grant No.(IFPHI-038-156-2020)Therefore,authors gratefully acknowledge technical and financial support from Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)systems.QC has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds.So,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data prediction.In this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical data.The output of optimization is either 0 or 1 i.e.,odd or even in QC.From this output value,the data is identified according to the class.Meanwhile,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter method.After the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by LTSA.The motivation of the minimal objective is to remain faster.However,some computations can be performed more efficiently by the proposed model.In testing,the test data was evaluated by minimal loss function.The outcomes were assessed in terms of accuracy,computational time,and so on.For this,databases like Lymphography,Dermatology,and Arrhythmia were used.
基金funded by Taif University,Saudi ArabiaThe author would like to acknowledge Deanship of Graduate Studies and Scientific Research,Taif University for funding this work.
文摘The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification.Initially,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation.Among these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 score.As a result,EfficientNetwas selected as the foundation for implementing Dynamic GradNet.Dynamic GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia detection.These adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and intervention.The model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD progression.The proposed model outperformed the baseline architectures,achieving remarkable generalizability across all stages.This findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification metrics.These findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based models.The model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early detection of neurodegenerative diseases.
文摘Medical image classification models often lack validation across diverse datasets,limiting their generalization in clinical settings.This study evaluates and optimizes an enhanced DenseNet-121 model,integrating dilated convolutions and Squeeze-and-Excitation(SE)blocks,for multi-modal medical image classification.We assess its robustness across Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and histopathology datasets,focusing on cross-domain and crossmodality performance.Experiments reveal strong in-domain results but significant degradation in cross-modality tasks(e.g.,MRI-to-CT accuracy drops to~0.5).To address this,we propose two strategies:(1)multi-modal joint training,which boosts cross-modality accuracy to 0.87,and(2)Cycle-Consistent Adversarial Networks(CycleGAN)-based modality translation,improving performance to 0.7.Gradient-weighted Class Activation Mapping(Grad-CAM)visualizations confirm the model’s focus on clinically relevant regions,enhancing interpretability.Findings highlight the superiority of multi-modal training while demonstrating CycleGAN’s utility when target-domain data is scarce.Future work should explore larger multi-center datasets and advanced domain adaptation to further improve robustness.
文摘Alzheimer’s disease(AD)is a significant challenge in modern healthcare,with early detection and accurate staging remaining critical priorities for effective intervention.While Deep Learning(DL)approaches have shown promise in AD diagnosis,existing methods often struggle with the issues of precision,interpretability,and class imbalance.This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence(XAI)techniques,in particular attention mechanisms,Gradient-Weighted Class Activation Mapping(Grad-CAM),and Local Interpretable Model-Agnostic Explanations(LIME),to improve bothmodel interpretability and feature selection.The study evaluates four different DL architectures(ResMLP,VGG16,Xception,and Convolutional Neural Network(CNN)with attention mechanism)on a balanced dataset of 3714 MRI brain scans from patients aged 70 and older.The proposed CNN with attention model achieved superior performance,demonstrating 99.18%accuracy on the primary dataset and 96.64% accuracy on the ADNI dataset,significantly advancing the state-of-the-art in AD classification.The ability of the framework to provide comprehensive,interpretable results through multiple visualization techniques while maintaining high classification accuracy represents a significant advancement in the computational diagnosis of AD,potentially enabling more accurate and earlier intervention in clinical settings.
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.
文摘The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of prepro- cessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations con- sidered include the discretization of numeric attributes, the selection of attribute subset（s）, and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.
基金supported in part by National Natural Science Foundation of China(No.62101136)Shanghai Municipal of Science and Technology Project(No.20JC1419500)+3 种基金Shanghai Sailing Program(No.21YF1402800)the Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01)ZJLab,Shanghai Center for Brain Science and Brain-Inspired Technology,the National Key R&D Program of China(No.2018YFB1305104)the Natural Science Foundation of Shanghai(No.21ZR1403600).
文摘Lung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening.However,LDCT images suffer from severe noise,largely influencing the performance of lung nodule classification.Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT(NDCT)images as the supervision for the denoising task,which is impractical in the context of clinical diagnosis using LDCT.To jointly train these two tasks in a unified framework without the NDCT images,this paper introduces a novel self-supervised method,termed strided Noise2Neighbors or SN2N,for blind medical image denoising and lung nodule classification,where the supervision is generated from noisy input images.More specifically,the proposed SN2N can construct the supervision infor-mation from its neighbors for LDCT denoising,which does not need NDCT images anymore.The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification.Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision.Moreover,our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.
