Restoration and Classification of Fundus Images : Diabetic Retinopathy Detection Using Progressive Transfer Learning

Abstract

Diabetic retinopathy (DR) is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of poor-quality fundus images—caused by factors such as inadequate illumination, noise, blurring and other motion artifacts yields a significant challenge for accurate DR screening. In this study, we propose progressive transfer learning (PTL) for multi-pass restoration to iteratively enhance the quality of degraded fundus images, ensuring more reliable DR screening.

Unlike previous methods that often focus on a single-pass restoration, multi-pass restoration via PTL can achieve a superior blind restoration performance that can even improve most of the good-quality fundus images in the dataset. Initially, a Cycle-GAN model is trained to restore low-quality images, followed by PTL induced restoration passes over the latest restored outputs to improve overall quality in each pass. The proposed method can learn blind restoration without requiring any paired data while surpassing its limitations by leveraging progressive learning and fine-tuning strategies to minimize distortions and preserve critical retinal features.

To evaluate PTL’s effectiveness on multi-pass restoration, we conducted experiments on DeepDRiD, a large-scale fundus imaging dataset specifically curated for diabetic retinopathy detection. Our result demonstrates state-of-the-art performance, showcasing PTL's potential as a superior approach to iterative image quality restoration.