Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder.

ABSTRACT: Diabetic retinopathy (DR) presents a significant concern among diabetic patients, often leading to vision impairment or blindness if left untreated. Traditional diagnosis methods are prone to human error, necessitating accurate alternatives. While various computer-aided systems have been developed to assist in DR detection, there remains a need for accurate and efficient methods to classify its stages. In this study, we propose a novel approach utilizing enhanced stacked auto-encoders for the detection and classification of DR stages. The classification is performed across one healthy (normal) stage and four DR stages: mild, moderate, severe, and proliferative. Unlike traditional CNN approaches, our method offers improved reliability by reducing time complexity, minimizing errors, and enha

SUBMITTER: Almas S 

PROVIDER: S-EPMC11747016 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature