Diabetic Retinopathy Detection: The Machine Learning Approach

Abstract

The objective of this paper is to provide a comprehensive review of the literature on Diabetic Retinopathy (DR) and evaluate various machine learning techniques for its detection. Diabetic retinopathy is a serious condition affecting individuals with diabetes, potentially leading to retinal damage and irreversible vision loss. Early detection is crucial for preventing progression and protecting vision.

We aim to identify the presence of diabetic retinopathy using advanced machine learning classification algorithms, including neural networks and support vector machines. This study will synthesize the methodologies employed, focusing on feature extraction, filtering, and pooling techniques used in image processing. Additionally, we will analyze the accuracy and outcomes of these approaches to assess their effectiveness in diagnosing diabetic retinopathy.