{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:39:53Z","timestamp":1780551593754,"version":"3.54.1"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"3s","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Taif University Researchers","award":["TURSP-2020\/79"],"award-info":[{"award-number":["TURSP-2020\/79"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>\n            <jats:bold>Diabetic retinopathy (DR)<\/jats:bold>\n            is one of the most common causes of vision loss in people who have diabetes for a prolonged period.\n            <jats:bold>Convolutional neural networks (CNNs)<\/jats:bold>\n            have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.\n          <\/jats:p>","DOI":"10.1145\/3469841","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T15:46:12Z","timestamp":1635263172000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":46,"title":["An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading"],"prefix":"10.1145","volume":"17","author":[{"given":"Mohammad","family":"Shorfuzzaman","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Shamim","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulmotaleb","family":"El Saddik","sequence":"additional","affiliation":[{"name":"University of Ottawa, Ottawa, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-019-00560-z"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(09)62124-3"},{"key":"e_1_3_1_4_2","first-page":"147","volume-title":"Proceedings of AMIA Joint Summits on Translational Science 2017","author":"Carson L.","year":"2017","unstructured":"L. Carson, Y. Darvin, G. Margaret, and L. Tony. 2017. Automated detection of diabetic retinopathy using deep learning. In Proceedings of AMIA Joint Summits on Translational Science 2017, 147\u2013155."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100377"},{"key":"e_1_3_1_6_2","unstructured":"J. Krause V. Gulshan E. Rahimy P. Karth K. Widner G. S. Corrado L. Peng and D. R. Webster. 2017. Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy 2017 CoRR abs\/1710.01711."},{"issue":"1","key":"e_1_3_1_7_2","first-page":"24 pages","article-title":"A multimodal, multimedia point-of-care deep learning framework for COVID-19 diagnosis","volume":"17","author":"Rahman M. A.","year":"2021","unstructured":"M. A. Rahman, M. S. Hossain, N. A. Alrajeh, and B. B. Gupta. 2021. A multimodal, multimedia point-of-care deep learning framework for COVID-19 diagnosis. ACM Trans. Multimedia Comput. Commun. 17, 1s, Article 18 (2021), 24 pages. DOI:https:\/\/doi.org\/10.1145\/3421725","journal-title":"ACM Trans. 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In Proceedings of the 12th International Conference on Intelligent Systems Design and Applications (ISDA). 826\u2013830."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20041005"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000458"},{"key":"e_1_3_1_23_2","author":"Hossain M. S.","unstructured":"M. S. Hossain, M. Al-Hammadi, and G. Muhammad. 2019. Automatic fruit classification using deep learning for industrial applications. 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