Hybrid Neural Networks and Machine Learning for Detection of Diabetic Retinopathy

 

 

 

Waleed Khalid Al-zubaidi1, Shokhan M. Al-Barzinji2, Zaid Sami Mohsen3, Omar Muthanna Khudhur4,*

 

1College of Biomedical Informatics, University of Information Technology and Communications, Baghdad, Iraq

 

2Department of Computer Networks Systems, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

 

3Department of Computer Science and Information Technology, College of Science, University of Hilla, 51001 Babil, Iraq

 

4Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq

 

Text Box: Abstract 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. Emails: dr.waleed.khalid@uoitc.edu.iq; shokhan.albarzinji@uoanbar.edu.iq; omar.m.khudhur@uoa.edu.iq; zaid.sami2020@gmail.com

 

Received: April 05, 2025 Revised: June 20, 2025 Accepted: August 21, 2025

 

Keywords: Deep learning; Diabetic Retinopathy; Machine learning; Support Vector Machine; EfficientNet-B0