Open AccessAutomated Detection Of Diabetic Retinopathy For Early Diagnosis Using Exudate Images
Author(s) -
P. Manimegalai,
S. Soundarya,
J.R. Aswath,
Sowmiya Murali,
N. Raja Lakshmi
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f1289.0986s319
Subject(s) - diabetic retinopathy , medicine , seriousness , retinal , ophthalmoscopy , retina , ophthalmology , diabetes mellitus , retinopathy , optometry , feature (linguistics) , artificial intelligence , computer science , psychology , endocrinology , neuroscience , linguistics , philosophy , political science , law
Retina plays a vital character in detection of various diseases in early point such as diabetes retinopathy which can be performed by analyzing the retinal images [6]. Diseased patients have to undergo periodic screening of eye. Standouts amongst the most predominant clinical indications of diabetic retinopathy are exudates [17]. To detect diabetic retinopathy in patients the ophthalmologist inspects the exudates by Ophthalmoscopy [17] where recognition of exudates is a vital diagnostic undertaking in which computer help may assume a noteworthy job. But intrinsic characteristics of retinal images detection process is difficult for the ophthalmologists. Here, we proposed another algorithm “Superpixel Multi-Feature Classification" for the programmed automatic recognition of retinal exudates successfully and to encourage ophthalmologist to give better patient finding experiencing diabetic retinopathy, advising them the level of seriousness ahead of time. The performance of algorithm has been compared as a result, the outcomes are effective and the sensitivity and specificity for our exudates identification is 80% and 91.28%, respectively [15].