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The study develops a deep learning framework to automatically classify retinal images into four groups: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. Using publicly available datasets (IDRiD and HRF), EfficientNet-B0, EfficientNet-B7, a from-scratch model, and AlexNet are benchmarked with multiple metrics. AlexNet achieves the best overall results and is validated via five-fold cross-validation, while SHAP-based interpretability highlights clinically relevant regions such as the optic disc and macula, supporting transparent and explainable screening.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-retinal-disease-classification-using-deep-learning-and-alexnet-with-statistical-models-analysis-research-article/445078/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-retinal-disease-classification-using-deep-learning-and-alexnet-with-statistical-models-analysis-research-article/445078.png","ImageObject",300,407,{"name":92,"@type":93},"OmBimo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-04","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which retinal disease categories does the proposed framework classify?","Question",{"text":112,"@type":113},"The framework classifies retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which model achieved the highest overall performance?",{"text":117,"@type":113},"AlexNet achieved the highest overall performance, with reported accuracy of 93.65% and strong sensitivity and specificity values.",{"name":119,"@type":110,"acceptedAnswer":120},"How is model interpretability assessed in the study?",{"text":121,"@type":113},"The study uses SHAP (SHapley Additive exPlanations) to identify retinal regions emphasized by the model, such as the optic disc and macula, to improve transparency and clinical trust.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},445078,1790742573,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090893581,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","OPEN ACCESS  \nCitation: Elkenawy EM, Khodadadi N, Gaber KS, Khodadadi E, Alhussan AA, Khafaga DS, et al. (2026) Automated retinal disease classification using deep learning and AlexNet with statistical models analysis. PLoS One 21(1):  \ne0338415 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0338415  \nEditor: Ali Mohammad Alqudah, University of Manitoba, CANADA  \nReceived: September 25, 2025  \nAccepted: November 23, 2025  \nPublished: January 6, 2026  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone](https://doi.org/10.1371/journal.pone). 0338415  \nRESEARCH ARTICLE  \nAutomated retinal disease classification using deep learning and AlexNet with statistical models analysis  \nEl-Sayed M. Elkenawy1,2 , Nima Khodadadi3 *, Khaled Sh. Gaber4 , Ehsan Khodadadi5  \n,  \nAmel Ali Alhussan6 , Doaa Sami Khafaga6 , Marwa M. Eid7,8  \n1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt, 2 Applied Science Research Center. Applied Science Private University, Amman, Jordan, 3 Department of Civil, Architectural and Environmental Engineering, University of Miami, Coral Gables, Florida, United States of America, 4 Computer Science and Intelligent Systems Research Center, Blacksburg, Virginia, United States of America, 5 Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, Arkansas, United States of America, 6 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, 7 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt, 8 Jadara Research Center, Jadara University, Irbid, Jordan  \n* [Nima.khodadadi@miami.edu](Nima.khodadadi@miami.edu)  \nAbstract  \nDiabetic Retinopathy, Cataract, and Glaucoma are major retinal diseases that require early detection to prevent irreversible vision loss. This study proposes a deep learning-based framework for the automated classification of retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. The dataset was compiled from publicly available retinal imaging databases, including IDRiD and HRF. Four convolutional neural network architectures—EfficientNet-B0, EfficientNetB7, a build-from-scratch model, and AlexNet—were evaluated using multiple performance metrics. Among these, AlexNet achieved the highest overall performance, attaining an accuracy of 93.65%, sensitivity of 94.39%, specificity of 98.05%, PPV of 93 .65%, NPV of 97 .95%, and an F1-score of 93 .74% . EfficientNet-B7 followed with an accuracy of 92 .82%, confirming the strength of transfer learning in retinal feature extraction. A five-fold cross-validation further validated AlexNet’s robustness, yielding a mean R2 of 0 .8891 with low variance, indicating consistent generalization across folds. Computational efficiency analysis showed that AlexNet achieved high diagnostic accuracy with a moderate processing time of approximately 14 minutes. Model interpretability using SHapley Additive exPlanations (SHAP) revealed that AlexNet highlighted clinically relevant retinal regions, such as the optic disc and macula, thereby enhancing transparency and clinical trust. In summary, the proposed framework demonstrates that interpretable deep learning models can deliver accurate, consistent, and explainable retinal disease classification, offering a foundation for real-time, AI-assisted ophthalmic screening systems.  \nCopyright: © 2026 Elkenawy et al. This isan open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any ","cbCaii91ntMCUCqe","https://ap.wps.com/l/cbCaii91ntMCUCqe","pdf",4645355,21,"English","# Abstract\n# Introduction","[{\"question\":\"Which retinal disease categories does the proposed framework classify?\",\"answer\":\"The framework classifies retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma.\"},{\"question\":\"Which model achieved the highest overall performance?\",\"answer\":\"AlexNet achieved the highest overall performance, with reported accuracy of 93.65% and strong sensitivity and specificity values.\"},{\"question\":\"How is model interpretability assessed in the study?\",\"answer\":\"The study uses SHAP (SHapley Additive exPlanations) to identify retinal regions emphasized by the model, such as the optic disc and macula, to improve transparency and clinical trust.\"}]","Automated Retinal Disease Classification using Deep Learning and AlexNet with Statistical Models Analysis - Research Article | PDF",1790710222,53]