Pub Date : 2025-01-01Epub Date: 2025-01-22DOI: 10.1016/j.cmpbup.2025.100179
Veena K.M. , Veena Mayya , Rashmi Naveen Raj , Sulatha V. Bhandary , Uma Kulkarni
Generative Adversarial Networks (GANs) are capturing the attention of peer researchers in paired or unpaired image-to-image translation applications, particularly in the domain of retinal image processing. Additionally, there are several effective image preprocessing techniques available that can significantly improve the performance of GANs. This study examines the impact of five different image preprocessing techniques - Green Channel, CLAHE on Green Channel, CLAHE on RGB channels, Green Channel Gaussian Convolution, and RGB Gaussian Convolution - on five different GAN variants: CycleGAN, Pix2Pix GAN, CUT GAN, FastCut GAN, and NICE GAN. The study involved conducting 30 experiments to assess the performances of these GAN variants in the image-to-image translation of dual-mode retinal images. The evaluation utilized Frechet Inception Distance (FID) and Kernel Inception Distance (KID) metric scores to measure the performance of the GAN variants. The results demonstrated that the CycleGAN model achieved the best performance with CLAHE on RGB preprocessed images, achieving the lowest FID and KID scores of 103.49 and 0.038, respectively. This investigation underscores the significant potential of image preprocessing techniques in enhancing the performance of GANs in image translation applications.
{"title":"Analysis of preprocessing for Generative Adversarial Networks: A case study on color fundoscopy to fluorescein angiography image-to-image translation","authors":"Veena K.M. , Veena Mayya , Rashmi Naveen Raj , Sulatha V. Bhandary , Uma Kulkarni","doi":"10.1016/j.cmpbup.2025.100179","DOIUrl":"10.1016/j.cmpbup.2025.100179","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) are capturing the attention of peer researchers in paired or unpaired image-to-image translation applications, particularly in the domain of retinal image processing. Additionally, there are several effective image preprocessing techniques available that can significantly improve the performance of GANs. This study examines the impact of five different image preprocessing techniques - Green Channel, CLAHE on Green Channel, CLAHE on RGB channels, Green Channel Gaussian Convolution, and RGB Gaussian Convolution - on five different GAN variants: CycleGAN, Pix2Pix GAN, CUT GAN, FastCut GAN, and NICE GAN. The study involved conducting 30 experiments to assess the performances of these GAN variants in the image-to-image translation of dual-mode retinal images. The evaluation utilized Frechet Inception Distance (FID) and Kernel Inception Distance (KID) metric scores to measure the performance of the GAN variants. The results demonstrated that the CycleGAN model achieved the best performance with CLAHE on RGB preprocessed images, achieving the lowest FID and KID scores of 103.49 and 0.038, respectively. This investigation underscores the significant potential of image preprocessing techniques in enhancing the performance of GANs in image translation applications.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-02-19DOI: 10.1016/j.cmpbup.2025.100184
Behnaz Motamedi, Balázs Villányi
Effective disease management necessitates the accurate and timely prediction of lung cancer and diabetes. Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. Nevertheless, enhancing boosting algorithms to achieve superior predictive accuracy continues to be a difficult task. This study proposes a Bayesian-Optimized GentleBoost Ensemble (BOGBEnsemble) to improve classification performance for diabetes prediction (DiP) and lung cancer prediction (LCP). Two Kaggle datasets—a diabetes dataset from multiple healthcare providers and a Survey Lung Cancer dataset from existent medical records—are utilized. Data preprocessing involves outlier removal, min–max normalization, class balancing, and Pearson correlation-based feature selection. The GentleBoost classifier is optimized using Bayesian hyperparameter tuning, focusing on learning rate and the number of weak learners, and is validated using 10-fold cross-validation. BOGBEnsemble is evaluated in comparison to leading models, such as Random Forest (RF), Adaptive Boosting (AdaBoost), Logistic Boosting (LogitBoost), Random Undersampling Boosting (RUSBoost), conventional GentleBoost, and Multi-Layer Perceptron (MLP) architectures. The DiP-BOGBEnsemble achieves a 99.26% accuracy, 98.94% precision, 99.60% recall, 99.26% F1-score, 99.46% F2-score, 98.51% MCC, 98.51 Kappa, 0.0041 FOR, and 22,606.75 DOR. The LC-BOGBEnsemble achieves a 96.51% accuracy, 97.83% precision, 94.76% recall, 96.28% F1-score, 95.36% F2-score, MCC of 93.03%, Kappa of 92.99, FOR of 0.0462, and DOR of 932.15. This study highlights the potential of BOGBEnsemble as a clinically viable tool for early disease detection and decision support, paving the way for more reliable and personalized healthcare strategies.
{"title":"A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis","authors":"Behnaz Motamedi, Balázs Villányi","doi":"10.1016/j.cmpbup.2025.100184","DOIUrl":"10.1016/j.cmpbup.2025.100184","url":null,"abstract":"<div><div>Effective disease management necessitates the accurate and timely prediction of lung cancer and diabetes. Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. Nevertheless, enhancing boosting algorithms to achieve superior predictive accuracy continues to be a difficult task. This study proposes a Bayesian-Optimized GentleBoost Ensemble (BOGBEnsemble) to improve classification performance for diabetes prediction (DiP) and lung cancer prediction (LCP). Two Kaggle datasets—a diabetes dataset from multiple healthcare providers and a Survey Lung Cancer dataset from existent medical records—are utilized. Data preprocessing involves outlier removal, min–max normalization, class balancing, and Pearson correlation-based feature selection. The GentleBoost classifier is optimized using Bayesian hyperparameter tuning, focusing on learning rate and the number of weak learners, and is validated using 10-fold cross-validation. BOGBEnsemble is evaluated in comparison to leading models, such as Random Forest (RF), Adaptive Boosting (AdaBoost), Logistic Boosting (LogitBoost), Random Undersampling Boosting (RUSBoost), conventional GentleBoost, and Multi-Layer Perceptron (MLP) architectures. The DiP-BOGBEnsemble achieves a 99.26% accuracy, 98.94% precision, 99.60% recall, 99.26% F1-score, 99.46% F2-score, 98.51% MCC, 98.51 Kappa, 0.0041 FOR, and 22,606.75 DOR. The LC-BOGBEnsemble achieves a 96.51% accuracy, 97.83% precision, 94.76% recall, 96.28% F1-score, 95.36% F2-score, MCC of 93.03%, Kappa of 92.99, FOR of 0.0462, and DOR of 932.15. This study highlights the potential of BOGBEnsemble as a clinically viable tool for early disease detection and decision support, paving the way for more reliable and personalized healthcare strategies.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-31DOI: 10.1016/j.cmpbup.2025.100210
Francesca Angelone , Alfonso Maria Ponsiglione , Roberto Grassi , Francesco Amato , Mario Sansone
Accurate segmentation of the breast is a fundamental step in computer-aided diagnosis (CAD) systems for mammography. In particular, several tasks, such as the classification of breast density, evaluation of correct positioning of the breast, and automatic detection and classification of suspicious lesions, preliminarily require an accurate segmentation of the pectoralis muscle. This study aims to propose an automatic breast segmentation algorithm that combines traditional methods with Deep Learning methods limited only to the border region between the muscle and the breast. This type of approach allows for reducing the risk of having good overall accuracy in multi-class classification that does not reflect adequate accuracy with respect to small classes, such as the pectoralis muscle in a mammographic image. The U-Net network was therefore implemented on patches extracted along the straight line with which the muscle-breast edge was first estimated. The predicted patches are repositioned to perform an edge refinement and obtain the total breast mask, using histogram-based thresholding to segment the background from the breast. The results show Dice values equal to 0.848 ± 0.196 and Jaccard index equal to 0.774 ± 0.227 for the single patches, and Dice values equal to 0.971 ± 0.011 and Jaccard index equal to 0.944 ± 0.022 for the entire breast segmentation.
{"title":"U-net based approach for pectoralis muscle segmentation in digital mammography","authors":"Francesca Angelone , Alfonso Maria Ponsiglione , Roberto Grassi , Francesco Amato , Mario Sansone","doi":"10.1016/j.cmpbup.2025.100210","DOIUrl":"10.1016/j.cmpbup.2025.100210","url":null,"abstract":"<div><div>Accurate segmentation of the breast is a fundamental step in computer-aided diagnosis (CAD) systems for mammography. In particular, several tasks, such as the classification of breast density, evaluation of correct positioning of the breast, and automatic detection and classification of suspicious lesions, preliminarily require an accurate segmentation of the pectoralis muscle. This study aims to propose an automatic breast segmentation algorithm that combines traditional methods with Deep Learning methods limited only to the border region between the muscle and the breast. This type of approach allows for reducing the risk of having good overall accuracy in multi-class classification that does not reflect adequate accuracy with respect to small classes, such as the pectoralis muscle in a mammographic image. The U-Net network was therefore implemented on patches extracted along the straight line with which the muscle-breast edge was first estimated. The predicted patches are repositioned to perform an edge refinement and obtain the total breast mask, using histogram-based thresholding to segment the background from the breast. The results show Dice values equal to 0.848 ± 0.196 and Jaccard index equal to 0.774 ± 0.227 for the single patches, and Dice values equal to 0.971 ± 0.011 and Jaccard index equal to 0.944 ± 0.022 for the entire breast segmentation.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144770953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-10-24DOI: 10.1016/j.cmpbup.2025.100223
Rosario Sarabia , Santiago Cepeda
{"title":"Comment on “Picture: A web application for decision support in glioma surgery” by van Genderen et al.","authors":"Rosario Sarabia , Santiago Cepeda","doi":"10.1016/j.cmpbup.2025.100223","DOIUrl":"10.1016/j.cmpbup.2025.100223","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100223"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-08-07DOI: 10.1016/j.cmpbup.2025.100202
Ayesha Humayun , Syed Shahabuddin , Saira Afzal , Ahmad Azam Malik , Suleman Atique , Usman Iqbal
{"title":"Retraction notice to “Healthcare strategies and initiatives about COVID19 in Pakistan: Telemedicine a way to look forward” [Computer Methods and Programs in Biomedicine Update, Volume 1, 2021, 100008]","authors":"Ayesha Humayun , Syed Shahabuddin , Saira Afzal , Ahmad Azam Malik , Suleman Atique , Usman Iqbal","doi":"10.1016/j.cmpbup.2025.100202","DOIUrl":"10.1016/j.cmpbup.2025.100202","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inhibiting the mammalian Target of Rapamycin (mTOR) represents a promising strategy in cancer therapy due to its crucial role in cell growth, survival, and metabolism. Using a variety of quantitative structure-activity relationship (QSAR) models, we present a comprehensive comparison of deep learning (DL) and classical machine learning (ML) techniques for modeling mTOR inhibitor activity. Unlike prior studies that focused on specific algorithms or limited descriptors, we benchmark a wide range of models, from traditional models like Random Forest, logistic regression, and SVM to modern algorithms like CNNs, GRUs, and LSTMs, on both descriptor-based features obtained by Dragon and descriptor-free inputs, including raw SMILES string, and Morgan fingerprints. This comprehensive analysis provides a robust foundation for using the best QSAR models specific to mTOR inhibition. Our findings revealed that while the Random Forest classifier achieved the highest accuracy among all models (0.9290 accuracy, 0.8940 F1-score, 0.9737 AUC), DL methods also demonstrated strong predictive capabilities, with nearly all models attaining an accuracy above 0.90. Among the DL models, CNN-QSAR using Morgan fingerprints achieved the highest accuracy (0.9271), F1-score (0.8950), and AUC (0.9696), demonstrating its effectiveness in capturing structural characteristics. The GRU-QSAR and LSTM-QSAR models, which utilized tokenized SMILES, achieved accuracies of 0.9002 and 0.9021, F1-scores of 0.8595 and 0.8603, and AUCs of 0.9270 and 0.9529, respectively, leveraging their ability to process sequential data.
{"title":"Integrative in Silico modeling for mTOR inhibition: From ridge classifiers to descriptor-free deep neural networks","authors":"Seyed Alireza Khanghahi , Hadi Kamkar , Seyedehsamaneh Shojaeilangari , Abdollah Allahverdi , Parviz Abdolmaleki","doi":"10.1016/j.cmpbup.2025.100208","DOIUrl":"10.1016/j.cmpbup.2025.100208","url":null,"abstract":"<div><div>Inhibiting the mammalian Target of Rapamycin (mTOR) represents a promising strategy in cancer therapy due to its crucial role in cell growth, survival, and metabolism. Using a variety of quantitative structure-activity relationship (QSAR) models, we present a comprehensive comparison of deep learning (DL) and classical machine learning (ML) techniques for modeling mTOR inhibitor activity. Unlike prior studies that focused on specific algorithms or limited descriptors, we benchmark a wide range of models, from traditional models like Random Forest, logistic regression, and SVM to modern algorithms like CNNs, GRUs, and LSTMs, on both descriptor-based features obtained by Dragon and descriptor-free inputs, including raw SMILES string, and Morgan fingerprints. This comprehensive analysis provides a robust foundation for using the best QSAR models specific to mTOR inhibition. Our findings revealed that while the Random Forest classifier achieved the highest accuracy among all models (0.9290 accuracy, 0.8940 F1-score, 0.9737 AUC), DL methods also demonstrated strong predictive capabilities, with nearly all models attaining an accuracy above 0.90. Among the DL models, CNN-QSAR using Morgan fingerprints achieved the highest accuracy (0.9271), F1-score (0.8950), and AUC (0.9696), demonstrating its effectiveness in capturing structural characteristics. The GRU-QSAR and LSTM-QSAR models, which utilized tokenized SMILES, achieved accuracies of 0.9002 and 0.9021, F1-scores of 0.8595 and 0.8603, and AUCs of 0.9270 and 0.9529, respectively, leveraging their ability to process sequential data.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144653529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-28DOI: 10.1016/j.cmpbup.2025.100215
Mohammed Ali Dawud , Mulugeta Hayelom Kalayou , Yitbarek Wasihun , Toyeb Yasine , Tewoflos Ayalew , Mulugeta Desalegn Kasaye
Background
Despite several hindering factors, such as limited internet access, unstable power supply, insufficient smart phone, lack of trainings regarding e-CHIS, affecting its implementation, electronic community health information system is a digitized type of community health information system content on a mobile platform that creates a logically interconnected programmatic content module for usage by health extension workers to register and provide high quality health services across the Nation. This study assessed electronic community health information system practice and its associated factors among health extension workers of south Wollo zone, Amhara, Ethiopia, 2024.
Methods
A facility-based cross-sectional study was conducted from 22 January 2024 to 01 April 2024. Study participants were selected by using simple random sampling for the quantitative study, and purposive sampling was employed for qualitative study. Data were collected using an interviewer-administered questionnaire. Data were entered into Epi Data version 4.6.1 and exported to SPSS version 26 for analysis. Descriptive statistics were summarized using figures and tables. Both bi-variable and multivariable logistic regression analyses were carried out. The level of significance was determined based on the AOR with 95 % CI and P-value at <0.05. Thematic analysis was used to analyze the data for qualitative part.
Results
In this study, 46 % of health extension workers showed Good practice of eCHIS. Respondents’ Knowledge, presence of electricity at the health facility, Availability of tablets for eCHIS, and work experience of participants were statistically significant associations with the practice of eCHIS.
Conclusion and Recommendation
In this study, the practice of eCHIS was 46 %. Variables such as availability of tablets, work experience, knowledge, and facility electricity supply were factors associated with electronic-health-information-system. Improving the knowledge of the participants would improve the e-CHIS practice.
尽管有一些阻碍因素，如有限的互联网接入，不稳定的电力供应，缺乏智能手机，缺乏有关电子卫生信息系统的培训，影响了它的实施，电子社区卫生信息系统是一种基于移动平台的数字化社区卫生信息系统内容，它创建了一个逻辑上相互关联的程序化内容模块，供卫生推广工作者在全国范围内注册和提供高质量的卫生服务。本研究评估了2024年埃塞俄比亚阿姆哈拉南Wollo区卫生推广工作者的电子社区卫生信息系统实践及其相关因素。方法于2024年1月22日至2024年4月1日进行以设施为基础的横断面研究。定量研究采用简单随机抽样，定性研究采用目的抽样。数据收集采用访谈者管理的问卷。数据输入Epi Data 4.6.1版本，导出到SPSS 26版本进行分析。描述性统计用图表进行汇总。进行了双变量和多变量logistic回归分析。以AOR确定显著性水平，95% CI， p值为0.05。定性部分数据采用主题分析法进行分析。结果有46%的卫生推广人员表现出良好的eCHIS行为。应答者的知识、卫生设施的电力供应、eCHIS药片的可获得性以及参与者的工作经验与eCHIS的实践具有统计上的显著相关性。结论与建议本研究中eCHIS的实施率为46%。诸如平板电脑的可用性、工作经验、知识和设施电力供应等变量是与电子卫生信息系统相关的因素。提高参与者的知识水平将会改善电子卫生信息系统的实践。
{"title":"Electronic community health information system practice and associated factors among health extension workers in South Wollo Zone, North East Ethiopia: Mixed study","authors":"Mohammed Ali Dawud , Mulugeta Hayelom Kalayou , Yitbarek Wasihun , Toyeb Yasine , Tewoflos Ayalew , Mulugeta Desalegn Kasaye","doi":"10.1016/j.cmpbup.2025.100215","DOIUrl":"10.1016/j.cmpbup.2025.100215","url":null,"abstract":"<div><h3>Background</h3><div>Despite several hindering factors, such as limited internet access, unstable power supply, insufficient smart phone, lack of trainings regarding e-CHIS, affecting its implementation, electronic community health information system is a digitized type of community health information system content on a mobile platform that creates a logically interconnected programmatic content module for usage by health extension workers to register and provide high quality health services across the Nation. This study assessed electronic community health information system practice and its associated factors among health extension workers of south Wollo zone, Amhara, Ethiopia, 2024.</div></div><div><h3>Methods</h3><div>A facility-based cross-sectional study was conducted from 22 January 2024 to 01 April 2024. Study participants were selected by using simple random sampling for the quantitative study, and purposive sampling was employed for qualitative study. Data were collected using an interviewer-administered questionnaire. Data were entered into Epi Data version 4.6.1 and exported to SPSS version 26 for analysis. Descriptive statistics were summarized using figures and tables. Both bi-variable and multivariable logistic regression analyses were carried out. The level of significance was determined based on the AOR with 95 % CI and P-value at <0.05. Thematic analysis was used to analyze the data for qualitative part.</div></div><div><h3>Results</h3><div>In this study, 46 % of health extension workers showed Good practice of eCHIS. Respondents’ Knowledge, presence of electricity at the health facility, Availability of tablets for eCHIS, and work experience of participants were statistically significant associations with the practice of eCHIS.</div></div><div><h3>Conclusion and Recommendation</h3><div>In this study, the practice of eCHIS was 46 %. Variables such as availability of tablets, work experience, knowledge, and facility electricity supply were factors associated with electronic-health-information-system. Improving the knowledge of the participants would improve the e-CHIS practice.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-22DOI: 10.1016/j.cmpbup.2025.100209
Shehu Mohammed, Neha Malhotra
Alzheimer’s Disease (AD) is a significant global health issue, and the current diagnostic techniques cannot diagnose the disease at its early stages, hence the difficulty of early therapeutic management. In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. The main goal is to combine multiple biomarkers: neuroimaging, CSF, genetic, and longitudinal cognitive data and develop a robust model for accurate early AD diagnosis. The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. The methodology involves federated learning and domain adaptation with GANs to integrate data from multiple centers with the patient’s privacy intact. It shows that the proposed multimodal model is superior to single-modality models with an AUC-ROC of 0.94 and reveals that hippocampal volume and plasma p-tau are the most informative biomarkers in the early diagnosis of AD. The study’s implications indicate that combining multimodal data improves diagnostic accuracy and clinical relevance by providing a roadmap to developing personalized medicine and better patient care. Future work will be aimed at increasing the variability of the dataset and the clinical trials to test the model to improve its applicability and performance in actual practice.
{"title":"Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data","authors":"Shehu Mohammed, Neha Malhotra","doi":"10.1016/j.cmpbup.2025.100209","DOIUrl":"10.1016/j.cmpbup.2025.100209","url":null,"abstract":"<div><div>Alzheimer’s Disease (AD) is a significant global health issue, and the current diagnostic techniques cannot diagnose the disease at its early stages, hence the difficulty of early therapeutic management. In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. The main goal is to combine multiple biomarkers: neuroimaging, CSF, genetic, and longitudinal cognitive data and develop a robust model for accurate early AD diagnosis. The importance of this work is in the opportunity to shift diagnostic paradigms by employing deep learning algorithms, including CNNs, LSTM networks, and GNNs to analyze spatial, temporal, and relational patterns across multi-modal data. The methodology involves federated learning and domain adaptation with GANs to integrate data from multiple centers with the patient’s privacy intact. It shows that the proposed multimodal model is superior to single-modality models with an AUC-ROC of 0.94 and reveals that hippocampal volume and plasma p-tau are the most informative biomarkers in the early diagnosis of AD. The study’s implications indicate that combining multimodal data improves diagnostic accuracy and clinical relevance by providing a roadmap to developing personalized medicine and better patient care. Future work will be aimed at increasing the variability of the dataset and the clinical trials to test the model to improve its applicability and performance in actual practice.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-07-14DOI: 10.1016/j.cmpbup.2025.100207
Sirmayanti , Pulung Hendro Prastyo , Mahyati
Diabetes mellitus, often called a silent killer, is a chronic condition characterized by insufficient insulin production and elevated blood sugar levels, leading to complications in vital organs such as the nerves, eyes, and kidneys. Machine learning is a powerful tool for predicting diabetes; however, noisy features can negatively impact its accuracy, making an effective feature selection essential. This study proposes an improved feature selection approach for diabetes prediction, leveraging the Grey Wolf Optimizer with an integrated Autophagy Mechanism (GWO-AM) on the Pima Indian Diabetes Dataset. The autophagy mechanism, inspired by cellular self-degradation and recycling, is incorporated into GWO to enhance exploration and exploitation. The method was also tested on glioma and lung cancer datasets to assess scalability. Comprehensive experiments demonstrate that GWO-AM significantly improves prediction accuracy while reducing the number of selected features. For the diabetes dataset, GWO-AM achieved an accuracy of 90.91 %, outperforming existing methods. It also excelled in the glioma and lung cancer datasets, highlighting its potential for application to other medical datasets.
{"title":"Enhancing diabetes prediction performance using feature selection based on grey wolf optimizer with autophagy mechanism","authors":"Sirmayanti , Pulung Hendro Prastyo , Mahyati","doi":"10.1016/j.cmpbup.2025.100207","DOIUrl":"10.1016/j.cmpbup.2025.100207","url":null,"abstract":"<div><div>Diabetes mellitus, often called a silent killer, is a chronic condition characterized by insufficient insulin production and elevated blood sugar levels, leading to complications in vital organs such as the nerves, eyes, and kidneys. Machine learning is a powerful tool for predicting diabetes; however, noisy features can negatively impact its accuracy, making an effective feature selection essential. This study proposes an improved feature selection approach for diabetes prediction, leveraging the Grey Wolf Optimizer with an integrated Autophagy Mechanism (GWO-AM) on the Pima Indian Diabetes Dataset. The autophagy mechanism, inspired by cellular self-degradation and recycling, is incorporated into GWO to enhance exploration and exploitation. The method was also tested on glioma and lung cancer datasets to assess scalability. Comprehensive experiments demonstrate that GWO-AM significantly improves prediction accuracy while reducing the number of selected features. For the diabetes dataset, GWO-AM achieved an accuracy of 90.91 %, outperforming existing methods. It also excelled in the glioma and lung cancer datasets, highlighting its potential for application to other medical datasets.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2025-08-23DOI: 10.1016/j.cmpbup.2025.100217
Firas Ibrahim AlZobi , Khalid Mansour , Ahmad Nasayreh , Ghassan Samara , Neda’a Alsalman , Ayah Bashkami , Aseel Smerat , Khalid M.O. Nahar
The heterogeneity of endometrial cancer tissue presents a significant obstacle to accurate automated classification using histopathological images. While ensemble methods are a promising alternative to single Convolutional Neural Networks (CNNs), we introduce PSO-SV (Particle Swarm Optimization–Soft Voting), a novel framework that adaptively fuses the outputs of MobileNetV2, VGG19, DenseNet121, Swin Transformer, and Vision Transformer (ViT). Our key innovation is the use of Particle Swarm Optimization to dynamically determine the optimal contribution of each model in a soft-voting ensemble. We validated PSO-SV on two datasets, the first one consists from 11,977 tiles from 95 whole-slide images (WSIs) obtained from The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) project, the other dataset consists of 3,302 images from 498 patients, which are categorized into four classes. The proposed framework achieved outstanding results, including 99.67% accuracy, a 99.67% F1-score, and an Area Under the Curve (AUC) of 99.9% on the first dataset and 99% for all metrics for the second dataset. It consistently outperformed all three individual CNNs and a traditional hard-voting ensemble, highlighting its ability to synergistically combine complementary model strengths. The PSO-SV framework offers a powerful and clinically promising approach for robust endometrial cancer classification.
{"title":"Optimized soft-voting CNN ensemble using particle swarm optimization for endometrial cancer histopathology classification","authors":"Firas Ibrahim AlZobi , Khalid Mansour , Ahmad Nasayreh , Ghassan Samara , Neda’a Alsalman , Ayah Bashkami , Aseel Smerat , Khalid M.O. Nahar","doi":"10.1016/j.cmpbup.2025.100217","DOIUrl":"10.1016/j.cmpbup.2025.100217","url":null,"abstract":"<div><div>The heterogeneity of endometrial cancer tissue presents a significant obstacle to accurate automated classification using histopathological images. While ensemble methods are a promising alternative to single Convolutional Neural Networks (CNNs), we introduce PSO-SV (Particle Swarm Optimization–Soft Voting), a novel framework that adaptively fuses the outputs of MobileNetV2, VGG19, DenseNet121, Swin Transformer, and Vision Transformer (ViT). Our key innovation is the use of Particle Swarm Optimization to dynamically determine the optimal contribution of each model in a soft-voting ensemble. We validated PSO-SV on two datasets, the first one consists from 11,977 tiles from 95 whole-slide images (WSIs) obtained from The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) project, the other dataset consists of 3,302 images from 498 patients, which are categorized into four classes. The proposed framework achieved outstanding results, including 99.67% accuracy, a 99.67% F1-score, and an Area Under the Curve (AUC) of 99.9% on the first dataset and 99% for all metrics for the second dataset. It consistently outperformed all three individual CNNs and a traditional hard-voting ensemble, highlighting its ability to synergistically combine complementary model strengths. The PSO-SV framework offers a powerful and clinically promising approach for robust endometrial cancer classification.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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