Abstract: Diabetic retinopathy (DR) is a leading cause of blindness, requiring early and accurate diagnosis. Although deep learning, particularly Convolutional Neural Networks (CNNs), has shown promising results in automating DR classification, selecting the optimal architecture and extracting effective features for specific clinical datasets remains a challenge. This study aims to conduct a comprehensive performance evaluation of six CNN architectures—DenseNet121, MobileNet, NasNet_Mobile, ResNet50, VGG16, and VGG19—for DR classification on a dataset from the Community Eye Hospital of South Sumatra Province. The main novelty of our approach lies in a specific preprocessing workflow that integrates grayscale conversion and Canny edge detection to enhance the visibility of critical retinal features, such as blood vessels and lesions, before classification. Using a dataset of 3000 fundus images across five classes (No_DR, Mild, Moderate, Severe, and Proliferative DR), the model was trained with data augmentation and the Adam optimizer. Experimental results indicate that the VGG16 architecture achieves a peak accuracy of 73%, outperforming baseline implementations from previous studies. This study highlights the potential of combining classical CNN models with tailored preprocessing for improved DR detection, thus providing a benchmark for model selection on similar clinical datasets. These findings highlight the robustness and stability of VGG16, demonstrating its suitability as an early DR screening tool.

Zaid Romegar Mair, Agus Harjoko, Rendra Gustriansyah, Septa Cahyani, Rudi Heriansyah, Indah Permatasari and Muhammad Haviz Irfani. “An Enhanced Deep Learning Framework for Diabetic Retinopathy Classification Using Multiple Convolutional Neural Network Architectures”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161176

@article{Mair2025,
title = {An Enhanced Deep Learning Framework for Diabetic Retinopathy Classification Using Multiple Convolutional Neural Network Architectures},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161176},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161176},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Zaid Romegar Mair and Agus Harjoko and Rendra Gustriansyah and Septa Cahyani and Rudi Heriansyah and Indah Permatasari and Muhammad Haviz Irfani}
}