Deep learning-powered segmentation and classification of diabetic retinopathy for enhanced diagnostic precision

This study addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes that potentially leads to blindness. We introduce a novel approach to DR detection using transfer learning, leveraging a single fundus photograph to automatically identify the disease’s stage. DR progresses through four stages, posing challenges for early detection, with existing methods often inefficient and prone to disagreements among clinicians. The proposed approach demonstrated in the APTOS 2019 Blindness Detection Competition employs convolutional neural networks (CNNs) and achieved a high quadratic weighted kappa score of 0.92546, highlighting its effectiveness in automatic DR detection and emphasizing the need for timely intervention. This paper first reviews related work, spanning classical computer vision methods to deep learning approaches, with a focus on CNNs. Transfer learning with CNN architectures is explored, showcasing promising results from various studies. Identifying two critical gaps in existing literature, the research emphasizes the need for comprehensive exploration into integrating pre-trained large language models (LLMs) with segmented image inputs for generating test/treatment recommendations. In addition, understanding the dynamic interactions among integrated components, including lesion segmentations, disease classification, and LLMs within web applications, remains essential. The objectives of the study include developing a comprehensive DR detection methodology, exploring and implementing model integration, evaluating performance through competition ranking, contributing significantly to DR detection methodologies, and identifying research gaps. The study encompasses revolutionizing DR detection by integrating cutting-edge technologies, focusing on transfer learning and various model integrations within web applications. The methodology covers data pre-processing, augmentation, segmentation using U-Net neural network architecture, and a detailed training process. The U-Net model demonstrates efficient segmentation of retinal structures with high accuracy and an impressive frames-per-second rate. The results highlight the model’s effectiveness in segmenting blood vessels, hard exudates, soft exudates, hemorrhages, microaneurysms, and the optical disc, with high Jaccard, F1, recall, precision, and accuracy scores. These findings underscore the model’s potential to enhance diagnostic capabilities in retinal pathology assessment, promising improved patient outcomes through timely diagnosis and intervention in combating DR.