Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction

Abstract

This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness. The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph, demonstrating high effectiveness with a quadratic weighted kappa score of 0.92546 in the APTOS 2019 Blindness Detection Competition. The paper reviews existing literature on DR detection, spanning classical computer vision methods to deep learning approaches, particularly focusing on CNNs. It identifies gaps in the research, emphasizing the lack of exploration in integrating pretrained large language models with segmented image inputs for generating recommendations and understanding dynamic interactions within a web application context.Objectives include developing a comprehensive DR detection methodology, exploring model integration, evaluating performance through competition ranking, contributing significantly to DR detection methodologies, and identifying research gaps.The methodology involves data preprocessing, data augmentation, and the use of a U-Net neural network architecture for segmentation. The U-Net model efficiently segments retinal structures, including blood vessels, hard and soft exudates, haemorrhages, microaneurysms, and the optical disc. High evaluation scores in Jaccard, F1, recall, precision, and accuracy underscore the model's potential for enhancing diagnostic capabilities in retinal pathology assessment.The outcomes of this research hold promise for improving patient outcomes through timely diagnosis and intervention in the fight against diabetic retinopathy, marking a significant contribution to the field of medical image analysis.