Computers in biology and medicine最新文献

{"title":"Rim learning framework based on TS-GAN: A new paradigm of automated glaucoma screening from fundus images","authors":"Arindam Chowdhury ,&nbsp;Ankit Lodh ,&nbsp;Rohit Agarwal ,&nbsp;Rahul Garai ,&nbsp;Ahana Nandi ,&nbsp;Narayan Murmu ,&nbsp;Sumit Banerjee ,&nbsp;Debashis Nandi","doi":"10.1016/j.compbiomed.2025.109752","DOIUrl":"10.1016/j.compbiomed.2025.109752","url":null,"abstract":"<div><div>Glaucoma detection from fundus images often relies on biomarkers such as the Cup-to-Disc Ratio (CDR) and Rim-to-Disc Ratio (RDR). However, precise segmentation of the optic cup and disc is challenging due to low-contrast boundaries and the interference of blood vessels and optic nerves. This article presents a novel automated framework for glaucoma detection that focuses on the rim structure as a biomarker, excluding the conventional reliance on CDR and RDR. The proposed framework introduces a Teacher–Student Generative Adversarial Network (TS-GAN) for precise segmentation of the optic cup and disc, along with a SqueezeNet for glaucoma detection. The Teacher model uses an attention-based CNN encoder–decoder, while the Student model incorporates Expectation Maximization to enhance segmentation performance. By combining implicit generative modeling and explicit probability density modeling, the TS-GAN effectively addresses the mode collapse problem seen in existing GANs. A rim generator processes the segmented cup and disc to produce the rim, which serves as input to SqueezeNet for glaucoma classification. The framework has been extensively tested on diverse fundus image datasets, including a private dataset, demonstrating superior segmentation and detection accuracy compared to state-of-the-art models. Results show its effectiveness in early glaucoma detection, offering higher accuracy and reliability. This innovative framework provides a robust tool for ophthalmologists, enabling efficient glaucoma management and reducing the risk of vision loss.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109752"},"PeriodicalIF":7.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning techniques to detect and analysis of multiple sclerosis through MRI: A systematic literature review.","authors":"Priyanka Belwal, Surendra Singh","doi":"10.1016/j.compbiomed.2024.109530","DOIUrl":"10.1016/j.compbiomed.2024.109530","url":null,"abstract":"<p><p>Deep learning (DL) techniques represent a rapidly advancing field within artificial intelligence, gaining significant prominence in the detection and analysis of various medical conditions through the analysis of medical data. This study presents a systematic literature review (SLR) focused on deep learning methods for the detection and analysis of multiple sclerosis (MS) using magnetic resonance imaging (MRI). The initial search identified 401 articles, which were rigorously screened, a selection of 82 highly relevant studies. These selected studies primarily concentrate on key areas such as multiple sclerosis, deep learning, convolutional neural networks (CNN), lesion segmentation, and classification, reflecting their alignment with the current state of the art. This review comprehensively examines diverse deep-learning approaches for MS detection and analysis, offering a valuable resource for researchers. Additionally, it presents key insights by summarizing these DL techniques for MS detection and analysis using MRI in a structured tabular format.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109530"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach.","authors":"Guilherme C Oliveira, Nemuel D Pah, Quoc C Ngo, Arissa Yoshida, Nícolas B Gomes, João P Papa, Dinesh Kumar","doi":"10.1016/j.compbiomed.2024.109565","DOIUrl":"10.1016/j.compbiomed.2024.109565","url":null,"abstract":"<p><strong>Background: </strong>Changes in voice are a symptom of Parkinson's disease and used to assess the progression of the condition. However, natural differences in the voices of people can make this challenging. Computerized binary speech classification can identify people with PD (PwPD), but its multiclass application to detect the severity of the disease remains difficult.</p><p><strong>Method: </strong>This study investigated six diadochokinetic (DDK) tasks, four features (phonation, articulation, prosody, and their fusion), and three machine learning models for four severity levels of PwPD. The four binary classifications were: (i) Normal vs Not Normal, (ii) Slight vs Not Slight, (iii) Mild vs Not Mild and (iv) Moderate vs. Not Moderate. The best task and features for each class were identified and the models were ensembled to develop a multiclass model to distinguish between Normal vs. Slight vs. Mild vs. Moderate.</p><p><strong>Results: </strong>For Normal vs Not-normal, logistic regression (LR) using the prosody from \"ka-ka-ka\" task, Random Forest (RF) using articulation from \"petaka\" for Slight vs Not Slight, RF for the fusion from \"ka-ka-ka\" for Mild vs Not Mild and Gradient Boosting (GB) using prosody from \"ta-ta-ta\" for Moderate vs Not Moderate gave the best results. Combining these using LR achieved an accuracy of 72%.</p><p><strong>Conclusion: </strong>Dividing the multiclass problem into four binary problems gives the optimum speech features for each class. This pilot study, conducted on a small public dataset, shows the potential of computerized speech analysis using DDK to evaluate the severity of Parkinson's disease voice symptoms.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109565"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent representation learning for classification of the Doppler ultrasound images.","authors":"Bo Li, Haoyu Chen, Zhongliang Xiang, Mengze Sun, Long Chen, Mingyan Sun","doi":"10.1016/j.compbiomed.2024.109575","DOIUrl":"10.1016/j.compbiomed.2024.109575","url":null,"abstract":"<p><p>The classification of Doppler ultrasound images plays an important role in the diagnosis of pregnancy. However, it is a challenging problem that suffers from a variable length of these images with a dimension gap between them. In this study, we propose a latent representation weights learning method (LRWL) for pregnancy prediction using Doppler ultrasound images. Unlike most existing methods, LRWL can handle a variable length of multiple images, especially with an irregular multi-image issue. Furthermore, a spatial interaction measurement (SIM) method is proposed to verify the hypothesis that LRWL can more accurately capture relationships among the images. The images, along with diagnostic indices and weights, are integrated as inputs to a deep learning (DL) model for pregnancy prediction. The study conducts comprehensive experiments involving classification tasks on real irregular reproduction datasets and two synthetic regular datasets. Results demonstrate that LRWL surpasses existing methods and is well-suited for irregular multi-image datasets. The proposed method can be effectively implemented using the limited-memory Broyden-Fletcher-Goldfarb-Shanno bound constraint (L-BFGS-B) and the alternating direction minimization (ADM) framework, exhibiting strong performance in terms of accuracy and convergence.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109575"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images","authors":"Xianyang Wang,&nbsp;Linlin Lv,&nbsp;Qingfeng Tang,&nbsp;Guangjun Wang,&nbsp;Enci Shang,&nbsp;Hang Zheng,&nbsp;Liangliang Zhang","doi":"10.1016/j.compbiomed.2024.109605","DOIUrl":"10.1016/j.compbiomed.2024.109605","url":null,"abstract":"<div><h3>Background</h3><div>Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. However, whether integrating radiomic features and deep features can improve the prediction performance of tumor information is unclear.</div></div><div><h3>Methods</h3><div>A feature fusion method based on radiomic features and revised deep features was proposed to predict tumor information. Radiomic features were extracted from the tumor region on ultrasound images, and the optimal radiomic features were subsequently selected based on Gini score. Revised deep features, which were extracted using the revised CNN models integrating prior information, were combined with radiomic features to build a logistic regression classifier for tumor prediction. The performance was evaluated using area under the receiver operating characteristic (ROC) curve (AUC).</div></div><div><h3>Results</h3><div>The results showed that the proposed feature fusion method (AUC = 0.9845) obtained better prediction performance than that based on radiomic features (AUC = 0.9796) or deep features (AUC = 0.9342).</div></div><div><h3>Conclusions</h3><div>Our results demonstrate that the proposed feature fusion framework integrating the radiomic features and revised deep features is an efficient method to improve the prediction performance of tumor information.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"Article 109605"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing multiple sclerosis diagnosis: A comparative study of electroencephalogram signal processing and entropy methods","authors":"Umut Aslan,&nbsp;Mehmet Feyzi Akşahin","doi":"10.1016/j.compbiomed.2024.109615","DOIUrl":"10.1016/j.compbiomed.2024.109615","url":null,"abstract":"<div><div>As one of the most common neurodegenerative diseases, Multiple sclerosis (MS) is a chronic immune-driven disorder that affects the central nervous system (CNS). Due to the variety of symptoms, accurately diagnosing MS demands rigorous attention to differential diagnosis, as various disorders can closely mimic its clinical and paraclinical features. Although MR imaging techniques are gold standards in diagnosing MS, the feasibility of advanced Electroencephalogram (EEG) signal processing methods is discussed in this study to detect patients with MS disorder. EEG signals from 50 individuals were evaluated through entropy-based methods. Sixteen distinct entropy methods were employed to extract features, which were used to train several machine-learning algorithms for classifying MS patients. Furthermore, each entropy method was individually evaluated to identify the most effective approach for MS diagnosis. A regional analysis of the EEG channels was conducted to determine the most informative regions for classification. The results indicated that the proposed method outperformed previous studies and achieved highly effective results in the classification of MS patients.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"Article 109615"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DAMNet: Dynamic mobile architectures for Alzheimer's disease","authors":"Meihua Zhou ,&nbsp;Tianlong Zheng ,&nbsp;Zhihua Wu ,&nbsp;Nan Wan ,&nbsp;Min Cheng","doi":"10.1016/j.compbiomed.2024.109517","DOIUrl":"10.1016/j.compbiomed.2024.109517","url":null,"abstract":"<div><div>Alzheimer's disease (AD) presents a significant challenge in healthcare, highlighting the necessity for early and precise diagnostic tools. Our model, DAMNet, processes multi-dimensional AD data effectively, utilizing only 7.4 million parameters to achieve diagnostic accuracies of 98.3 % in validation and 99.9 % in testing phases. Despite a 20 % pruning rate, DAMNet maintains consistent performance with less than 0.2 % loss in accuracy. The model also excels in handling 3D (Three-Dimensional) MRI data, achieving a 95.7 % F1 score within 805 s during a rigorous three-fold validation over 200 epochs. Furthermore, we introduce a novel parallel intelligent framework for early AD detection that improves feature extraction and incorporates advanced data management and control. This framework sets a new benchmark in intelligent, precise medical diagnostics, adeptly managing both 2D (Two-Dimensional) and 3D imaging data.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"Article 109517"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early detection of high blood pressure from natural speech sounds with graph diffusion network","authors":"Haydar Ankışhan ,&nbsp;Haydar Celik ,&nbsp;Haluk Ulucanlar ,&nbsp;Bülent Mustafa Yenigün","doi":"10.1016/j.compbiomed.2024.109591","DOIUrl":"10.1016/j.compbiomed.2024.109591","url":null,"abstract":"<div><div>This study presents an innovative approach to cuffless blood pressure prediction by integrating speech and demographic features. With a focus on non-invasive monitoring, especially in remote regions, our model harnesses speech signals and demographic data to accurately estimate blood pressure. We found a strong correlation between our predictive model and early-stage high blood pressure, highlighting its potential for early detection. Central to our investigation is the Graph Diffusion Network (GDN) model, achieving exceptional performance with an R² score of 0.96 and a Pearson correlation coefficient (PCC) of 0.98. In early-stage hypertension detection, the GDN model achieved an F1-Score of 0.8735 ± 0.10 and accuracy of 0.8896 ± 0.11. Additionally, without considering demographic features, the model still performed well, with an R² of 0.740 and PCC of 0.764 when used alone. These results emphasize the value of combining speech and demographic features, offering a promising, non-invasive solution for blood pressure monitoring.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"Article 109591"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging deep transfer learning and explainable AI for accurate COVID-19 diagnosis: Insights from a multi-national chest CT scan study","authors":"Nhat Truong Pham ,&nbsp;Jinsol Ko ,&nbsp;Masaud Shah ,&nbsp;Rajan Rakkiyappan ,&nbsp;Hyun Goo Woo ,&nbsp;Balachandran Manavalan","doi":"10.1016/j.compbiomed.2024.109461","DOIUrl":"10.1016/j.compbiomed.2024.109461","url":null,"abstract":"<div><div>The COVID-19 pandemic has emerged as a global health crisis, impacting millions worldwide. Although chest computed tomography (CT) scan images are pivotal in diagnosing COVID-19, their manual interpretation by radiologists is time-consuming and potentially subjective. Automated computer-aided diagnostic (CAD) frameworks offer efficient and objective solutions. However, machine or deep learning methods often face challenges in their reproducibility due to underlying biases and methodological flaws. To address these issues, we propose XCT-COVID, an explainable, transferable, and reproducible CAD framework based on deep transfer learning to predict COVID-19 infection from CT scan images accurately. This is the first study to develop three distinct models within a unified framework by leveraging a previously unexplored large dataset and two widely used smaller datasets. We employed five known convolutional neural network architectures, both with and without pretrained weights, on the larger dataset. We optimized hyperparameters through extensive grid search and 5-fold cross-validation (CV), significantly enhancing the model performance. Experimental results from the larger dataset showed that the VGG16 architecture (XCT-COVID-L) with pretrained weights consistently outperformed other architectures, achieving the best performance, on both 5-fold CV and independent test. When evaluated with the external datasets, XCT-COVID-L performed well with data with similar distributions, demonstrating its transferability. However, its performance significantly decreased on smaller datasets with lower-quality images. To address this, we developed other models, XCT-COVID-S1 and XCT-COVID-S2, specifically for the smaller datasets, outperforming existing methods. Moreover, eXplainable Artificial Intelligence (XAI) analyses were employed to interpret the models’ functionalities. For prediction and reproducibility purposes, the implementation of XCT-COVID is publicly accessible at <span><span>https://github.com/cbbl-skku-org/XCT-COVID/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"Article 109461"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142779593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing cancer diagnosis and prognostication through deep learning mastery in breast, colon, and lung histopathology with ResoMergeNet","authors":"Chukwuebuka Joseph Ejiyi ,&nbsp;Zhen Qin ,&nbsp;Victor K. Agbesi ,&nbsp;Ding Yi ,&nbsp;Abena A. Atwereboannah ,&nbsp;Ijeoma A. Chikwendu ,&nbsp;Oluwatoyosi F. Bamisile ,&nbsp;Grace-Mercure Bakanina Kissanga ,&nbsp;Olusola O. Bamisile","doi":"10.1016/j.compbiomed.2024.109494","DOIUrl":"10.1016/j.compbiomed.2024.109494","url":null,"abstract":"<div><div>Cancer, a global health threat, demands effective diagnostic solutions to combat its impact on public health, particularly for breast, colon, and lung cancers. Early and accurate diagnosis is essential for successful treatment, prompting the rise of Computer-Aided Diagnosis Systems as reliable and cost-effective tools. Histopathology, renowned for its precision in cancer imaging, has become pivotal in the diagnostic landscape of breast, colon, and lung cancers. However, while deep learning models have been widely explored in this domain, they often face challenges in generalizing to diverse clinical settings and in efficiently capturing both local and global feature representations, particularly for multi-class tasks. This underscores the need for models that can reduce biases, improve diagnostic accuracy, and minimize error susceptibility in cancer classification tasks. To this end, we introduce ResoMergeNet (RMN), an advanced deep-learning model designed for both multi-class and binary cancer classification using histopathological images of breast, colon, and lung. ResoMergeNet integrates the Resboost mechanism which enhances feature representation, and the ConvmergeNet mechanism which optimizes feature extraction, leading to improved diagnostic accuracy. Comparative evaluations against state-of-the-art models show ResoMergeNet’s superior performance. Validated on the LC-25000 and BreakHis (400<span><math><mrow><mo>×</mo></mrow></math></span> and 40<span><math><mrow><mo>×</mo></mrow></math></span> magnifications) datasets, ResoMergeNet demonstrates outstanding performance, achieving perfect scores of 100 % in accuracy, sensitivity, precision, and F1 score for binary classification. For multi-class classification with five classes from the LC25000 dataset, it maintains an impressive 99.96 % across all performance metrics. When applied to the BreakHis dataset, ResoMergeNet achieved 99.87 % accuracy, 99.75 % sensitivity, 99.78 % precision, and 99.77 % F1 score at 400× magnification. At 40× magnification, it still delivered robust results with 98.85 % accuracy, sensitivity, precision, and F1 score. These results emphasize the efficacy of ResoMergeNet, marking a substantial advancement in diagnostic and prognostic systems for breast, colon, and lung cancers. ResoMergeNet’s superior diagnostic accuracy can significantly reduce diagnostic errors, minimize human biases, and expedite clinical workflows, making it a valuable tool for enhancing cancer diagnosis and treatment outcomes.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"Article 109494"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142784360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}