2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 169-186, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Waleed Khalid Al-zubaidi 1 , Shokhan M. Al-Barzinji 2 , Zaid Sami Mohsen 3 , Omar Muthanna Khudhur 4 *
Doi: https://doi.org/10.54216/JISIoT.180213
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.
Deep learning , Diabetic Retinopathy , Machine learning , Support Vector Machine , EfficientNet-B0
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