A Hybrid Approach Combining Deep CNN Features with Classical Machine Learning for Diabetic Retinopathy Diagnosis

One of the main causes of vision impairment is diabetic retinopathy (DR), a common and dangerous consequence of diabetes that damages the retinal blood vessels. Preventing irreversible vision loss requires early detection of DR. Recent developments demonstrate how artificial intelligence (AI), and in particular deep learning (DL), can automate the classification of retinal images for the diagnosis of DR. In this study, a hybrid model is proposed that combines deep learning-based feature extraction with classical machine learning classifiers for robust medical image analysis. After using preprocessing methods to lower background noise, this study investigates the use of Convolutional Neural Networks (CNNs) for extracting discriminative features from DR images. To improve image contrast and highlight vascular features, the preprocessing pipeline uses morphological top-hat filtering and green channel extraction. Furthermore, transfer learning was applied to enhance feature representation. The tuned Radial Basis Function Support Vector Machine (RBF-SVM) had the greatest classification accuracy of 85% among the machine learning (ML) classifiers that were assessed, including Random Forest (RF), Gradient Boosting (GB), and RBF-SVM. These findings demonstrate the potential of hybrid AI-driven approaches and domain-specific medical image analysis in providing reliable and efficient automated DR detection.

@article{Kaur2025,
  title     = {A Hybrid Approach Combining Deep CNN Features with Classical Machine Learning for Diabetic Retinopathy Diagnosis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Amandeep Kaur and Simranjit Singh and Hardeep Singh and Sarveshwar Bharti and Jai Sharma and Himanshi Sharma},
  doi       = {10.14569/IJACSA.2025.0160827},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160827}
}