AI-Driven Diabetic Retinopathy Detection: Advancements In Early Diagnosis

Abstract

Diabetic retinopathy (DR), a dangerous side effect of diabetes mellitus, is the main factor causing vision impairment worldwide. To stop irreparable retinal damage, prompt detection and treatment are essential. The goal of this project is to improve the efficacy and precision of screening procedures by developing and implementing a machine-learning-based strategy for the early identification of diabetic retinopathy. To extract pertinent features including microaneurysms, exudates, and hemorrhages, high-resolution retinal pictures undergo preprocessing. To automatically extract complex patterns and minute anomalies from retinal pictures, we use convolutional neural networks. The model has strong performance across a range of severity levels due to its training on a varied dataset that includes photos from different stages of diabetic retinopathy. This finding is important because it has the potential to transform the screening process for diabetic retinopathy, allowing for prompt intervention and lowering the risk of vision loss. The amalgamation of machine learning and clinical data lays the groundwork for a more intricate and customized method of diagnosing diabetic retinopathy, hence augmenting the wider domain of precision medicine and digital healthcare.