Retinal image-based disease classification using hybrid deep architecture with improved image features.
Journal:
International ophthalmology
Published Date:
Aug 5, 2025
Abstract
OBJECTIVE: Ophthalmologists use retinal fundus imaging as a useful tool to diagnose retinal issues. Recently, research on machine learning has concentrated on disease diagnosis. However, disease detection is less accurate, more likely to be misidentified, and often takes a long time to get the right conclusions. This study suggested a new hybrid Deep Learning (DL) approach for retinal illness classification using retinal images to overcome these problems. Three crucial stages are included in this proposed study: preprocessing, feature extraction, and disease classification.
