A Survey of Deep Learning Models to Detect and Classify Eye Disorders

Various advancements in the machine learning methods helps in early identification of eye diseases by using an automated system. It has significant advantages over the manual detection. This article offers a thorough analysis of deep learning and image processing-based automated methods for detecting eye diseases. Deep learning has recently risen to prominence as one of the methods used most frequently to improve performance in a variety of fields, including medical image analysis and classification. In the analysis of medical images, convolutional neural networks are a more prominent and effective deep learning technique. Deep learning strategies for eye illness detection and classification have been researched and examined for this paper. The accessible datasets for the colour fundus retina have also been examined. Readers will be able to recognise gaps in existing research and comprehend the difficulties that eye disease presents from the perspectives of image processing and machine learning.