{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:17:26Z","timestamp":1792167446560,"version":"4.4.0"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate and early classification of retinal diseases such as diabetic retinopathy, cataract, and glaucoma is essential for preventing vision loss and improving clinical outcomes. Manual diagnosis from fundus images is often time-consuming and error-prone, motivating the development of automated solutions. This study proposes a deep learning-based classification model using a pretrained EfficientNetB3 architecture, fine-tuned on a publicly available Kaggle retinal image dataset. The model categorizes images into four classes: cataract, diabetic retinopathy, glaucoma, and healthy. Key enhancements include transfer learning, data augmentation, and optimization via the Adam optimizer with a cosine annealing scheduler. The proposed model achieved a classification accuracy of 95.12%, with a precision of 95.21%, recall of 94.88%, F1-score of 95.00%, Dice Score of 94.91%, Jaccard Index of 91.2%, and an MCC of 0.925. These results demonstrate the model\u2019s robustness and potential to support automated retinal disease diagnosis in clinical settings.<\/jats:p>","DOI":"10.3390\/jimaging11080279","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T07:56:06Z","timestamp":1755590166000},"page":"279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Fundus Image-Based Eye Disease Detection Using EfficientNetB3 Architecture"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8043-9247","authenticated-orcid":false,"given":"Rahaf","family":"Alsohemi","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11911, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1376-9607","authenticated-orcid":false,"given":"Samia","family":"Dardouri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11911, Saudi Arabia"},{"name":"InnoV\u2019COM Laboratory-Sup\u2019Com, University of Carthage, Ariana 2083, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1038\/s41433-018-0064-9","article-title":"Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence","volume":"32","author":"Rajalakshmi","year":"2018","journal-title":"Eye"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","article-title":"Convolutional neural networks for diabetic retinopathy","volume":"90","author":"Pratt","year":"2016","journal-title":"Procedia Comput. 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