Open AccessLiterature Survey on Diabetic Retinopathy Classification Using Deep Learning
Author(s) -
Alfiya Md. Shaikh
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37733
Subject(s) - diabetic retinopathy , categorization , blindness , fundus (uterus) , optometry , artificial intelligence , medicine , computer science , retinopathy , incidence (geometry) , identification (biology) , retinal , ophthalmology , diabetes mellitus , mathematics , botany , geometry , biology , endocrinology
Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.