Vision-Threatening Diabetic Retinopathy Detection Using EfficientNetB4: A Two-Stage Transfer Learning Approach

Vannala Harish1, Dr.G.Thirupati 2

1M.TECH Student, Department Of CSE, SVS Group of Institutions (Autonomous), Bheemaram, Hanmakonda, Telangana

2Assistant Professor, Department Of CSE, SVS Group of Institutions (Autonomous), Bheemaram, Hanmakonda, Telangana

ABSTRACT

Threatening Diabetic Retinopathy (VTDR) remains an advanced stage of ophthalmic complications secondary to diabetes and is characterized as a major, preventable cause of irreversible vision loss. In this paper, we propose an automated deep learning framework to classify retinal fundus images into Non-VTDR (Healthy/Mild DR) and VTDR (Moderate/Severe/Proliferate DR). We use a two-stage transfer learning with EfficientNetB4 backbone architecture set up during training with ImageNet weights. A dataset of 2,750 retinal images is split into balanced training (70%), validation(20%), and testing (10%) sets. We applied several data augmentation techniques using rotation, zoom, brightness and horizontal flipping to improve model generalization. We freeze all base layers for 10 epochs, use class-weighted binary cross-entropy loss, then fine-tune the top 120 layers of VGG16 (20 epochs). Experimental results show a total test accuracy of 86.18% proving the efficiency of our method for automating VTDR screening. Conclusion: A network that maintains relatively high accuracy, is easy-to-use, and takes only a few milliseconds to classify an image can be easily integrated into clinical workflows to provide ophthalmologists with decision support for finding sight-threatening disease, especially in underprivileged areas where access to specialist care is limited.

Keywords: Deep Learning, EffientNetB4, Transfer Learning, Machine learning (ML), Medical image classification, Retinal Fundus Imaging,, Computer-Aided Diagnosis,(CAC), Binary Classification Multiclass Classification Diabetic Eye Disease Screening