Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3296 2018 2018 Informatics Institute for Postgraduate Studies, Iraqi Commission for Computers and Informatics, Baghdad, Iraq Raghad. Raghad. Department of Information and Communication Engineering, University of Baghdad, Baghdad, Iraq Ali. H. Hamad Retinopathy is a progressive and common retinal disease that most progressive diabetics suffer from and causes blood vessels in the retina to swell and leak blood and fluid. This condition requires timely diagnosis via medical experts to prevent causing visual loss among patients. To enhance the feasibility of checking many persons, diverse deep-learning schemes have recently been developed for diabetic retinopathy detection. In this paper, retinopathy image detection system based on diverse deep learning schemes (VGG-19, DenseNet-121, and EfficientNet-B6) has been presented. The implemented deep learning schemes with multi-label classification are trained and tested using the Asia Pacific Tele Ophthalmology Society (APTOS-2019) dataset, and the two combined datasets Indian Diabetic Retinopathy Image Dataset (IDRiD) and Messidor-2. The system outcomes of classification are exhibited as sensitivity, precision, F1Score, and accuracy measurements, and the system performance is compared with recently existing related systems. The attained outcomes indicate that the implemented EfficientNetB6 network outperforms peers’ schemes and related systems via realizing supreme accuracy using balanced multi-class retinopathy datasets. 2025 2025 279 293 10.54216/FPA.170221 https://www.americaspg.com/articleinfo/3/show/3296