AI-Driven Early Prediction Of Eye Disorder

Diabetic retinopathy (DR) arises when diabetes mellitus damages the blood vessels in the retina, the crucial part of the eye responsible for image capture. Without timely intervention, this damage can lead to vision impairment or even blindness. Given the cost and time constraints associated with manual examination of fundus images by trained medical professionals, there’s a pressing need for automated DR detection systems. AI-enabled teleophthalmology leveraging 5G/6G technology can enhance eye care accessibility, particularly in remote regions. Transfer learning plays a pivotal role in medical image analysis by facilitating the development of accurate and efficient models, especially when data and computational resources are limited. This paper presents a modification of the VGG16 network for DR detection, incorporating a feature selection technique. By fine-tuning network parameters and selecting superior features, our proposed approach significantly enhances performance. Classification using machine learning models yields promising results over a benchmark DR dataset, with our modified VGG16 network achieving an impressive accuracy of 98.4%, surpassing the standard VGG16 and ResNet18 network along with state-of-the-art methods.