Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/4025 2019 2019 College of Biomedical Informatics, University of Information Technology and Communications, Baghdad, Iraq Omar Omar Department of Computer Networks Systems, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq Shokhan M. Al Al-Barzinji Department of Computer Science and Information Technology, College of Science, University of Hilla, 51001 Babil, Iraq Zaid Sami Mohsen Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Omar Muthanna Khudhur Diabetic retinopathy (DR) is one of the most common causes of blindness in the world, and early detection plays an important role in therapy. In this paper, we introduce a hybrid framework with the merger of sophisticated image processing techniques and deep learning models for automated DR detection from retinal fundus images. Information starts with an extensive preprocessing pipeline, which includes bilateral filtering for noise reduction, removal of artifacts, adaptive contrast enhancement and a precise segmentation in the U-Net architecture. To increase model robustness, random rotation augmentation was used to mimic different imaging positions. GLCM analysis is used to extract texture features capturing important lesion-related patterns, and deep features are extracted using a fine-tuned EfficientNet-B0 model. The hybrid feature set is then modelled by a Support Vector Machine (SVM) with the radial basis function kernel and optimized with cross-validation and hyperactive parameters. Experiments show our model can well solve the image heterogeneity problem and yields a high level of accuracy in diagnosis and grading corresponding severity requirements of DR stage. 2026 2026 169 186 10.54216/JISIoT.180213 https://www.americaspg.com/articleinfo/18/show/4025