Transfer Learning Approach Using ImageNet CNN for Diabetic Retinopathy Detection and Classification from Fundus Images - ShadullahKhan, Pathan AfzalKhan; Holambe, Sushilkumar N (International Journal of Computer Sciences and Engineering, 2025)
Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness worldwide, and its early detection plays a vital role in reducing vision impairment. Recent advances in deep learning have demonstrated significant potential in automating the screening and classification of retinal diseases. This paper presents a transfer learning-based approach for the detection and classification of DR from fundus images using a pre-trained Convolutional Neural Network (CNN) model trained on the ImageNet dataset. By fine-tuning the network with a large-scale fundus image dataset, the proposed method effectively leverages learned visual representations to capture intricate retinal features. The experimental results indicate high accuracy in distinguishing between different severity levels of DR, outperforming conventional machine learning techniques. The findings highlight that transfer learning not only reduces training time but also enhances model generalization, making it a reliable tool for computer-aided diagnosis in ophthalmology.