  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.
