Eye Disease Recognition Using Deep Learning

Abstract

The diseases of the eyes are among the leading threats to the health of individuals around the world and, in many cases, lead to the complete loss of vision if left untreated. Ocular diseases have been diagnosed in the early stages using recent developments in medical image analysis, particularly CNN's. This paper presents a brief literature survey on trends in deep learning for the diagnosis of ocular diseases with a special emphasis on the possibilities of CNN in the diagnosis of ocular diseases. It focuses on the development and critical assessment of DL models trained from different datasets to effectively detect different ocular diseases including DR, glaucoma, and cataracts. Because of intense architectural designs in training techniques and improvement methodologies, the models can perform high accuracy rates hence the role of deep learning in reshaping the ophthalmology field. Besides the strictly technical aspects of the work, the findings emphasize that clinic density increases health quality because it enables early disease diagnosis, thus eliminating vision loss. It also covers ethical issues and useful considerations on practice in healthcare facilities. the work gives an overview of the revolution in identifying ocular diseases by deep learning and a bright future in improving the eye healthcare system across the world.