Open AccessLesion Detection and Classification techniques for Diabetic Retinopathy
Author(s) -
Jyoti Sawant,
Sachin Naik,
Santosh S. Chowhan
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6071.029320
Subject(s) - diabetic retinopathy , retinopathy , blindness , abnormality , retina , medicine , computer science , artificial intelligence , diabetes mellitus , disease , optometry , segmentation , ophthalmology , pattern recognition (psychology) , pathology , neuroscience , psychology , psychiatry , endocrinology
Diabetes is a worldwide spread disease which is increasing rapidly and found in all age people. Diabetic Retinopathy is a retinal abnormality caused by diabetes. Which can lead to permanent vision loss or blindness. As Diabetic Retinopathy pathology damages retina without any early symptoms, it is very important to do the regular screening of retina and detection of Retinopathy. Ophthalmologist does the identification of Retinopathy manually which is time consuming and error prone. Hence, there is a need for early and correct automatic detection of Diabetic Retinopathy. Many researches have done for detection using Image Processing, Artificial Intelligence, Neural Network and Machine Learning. This paper presents a review on Diabetic Retinopathy Detection systems. This review highlights the public datasets available for the evaluation of the detection systems with different segmentation and classification techniques. We have discussed the analysis of different classification and segmentation techniques used in DR detection.