A Study on Diabetic Retinopathy Detection,Segmentation and Classification using Deep and Machine Learning Techniques

被引:3
作者
Sesikala, Bapatla [1 ]
Harikiran, Jonnadula [1 ]
SaiChandana, Bolem [1 ]
机构
[1] VIT AP Univ, Sch CSE, Amaravathi, India
来源
2022 6TH INTERNATIONAL CONFERENCE ON TRENDS IN ELECTRONICS AND INFORMATICS, ICOEI 2022 | 2020年
关键词
Machine Learning; classification; segmentation; Diabetic retinopathy; fundus image; Deep Learning;
D O I
10.1109/ICOEI53556.2022.9776690
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
DR is a severe eye illness that is quickly expanding around the world. It happens when blood sugar rises, causing problems wi th the kidneys, eyes, and heart. In retina, eye condition of Diabetic Retinopathy (DR) caused by the blood vesselsbursting, which occurs as diabetes progresses. It is assumed to be the major cause of visual impairment since it arises without presenting any symptoms in the beginning stages. Moreover, extract characteristics from fundus images and identify the location of DR, evaluate its intensity, and divide associated lesionsusing deep and machine learning methods. In this review paper discusses the methods exploded in DR detection, segmentation and classification using deep and machine learning techniques along with the significance and limitations are encounter, as well as discuss future directions to overcome their limitations.
引用
收藏
页码:1419 / 1424
页数:6
相关论文
共 24 条
[1]  
[Anonymous], 2018, AMIA SUMM TRANSL SCI
[2]   A deep learning interpretable classifier for diabetic retinopathy disease grading [J].
de la Torre, Jordi ;
Valls, Aida ;
Puig, Domenec .
NEUROCOMPUTING, 2020, 396 :465-476
[3]  
delaPava M, 2021, Arxiv, DOI arXiv:2110.07745
[4]   Deep Bayesian baseline for segmenting diabetic retinopathy lesions: Advances and challenges [J].
Garifullin, Azat ;
Lensu, Lasse ;
Uusitalo, Hannu .
COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 136
[5]   Diabetic retinopathy classification based on multipath CNN and machine learning classifiers [J].
Gayathri, S. ;
Gopi, Varun P. ;
Palanisamy, P. .
PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE, 2021, 44 (03) :639-653
[6]   A lightweight CNN for Diabetic Retinopathy classification from fundus images [J].
Gayathri, S. ;
Gopi, Varun P. ;
Palanisamy, P. .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2020, 62
[7]   A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification [J].
Gurcan, Omer Faruk ;
Beyca, Omer Faruk ;
Dogan, Onur .
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2021, 14 (01) :1132-1141
[8]   An Intelligent Particle Swarm Optimization with Convolutional Neural Network for Diabetic Retinopathy Classification Model [J].
Jayanthi, J. ;
Jayasankar, T. ;
Krishnaraj, N. ;
Prakash, N. B. ;
Britto, A. Sagai Francis ;
Kumar, K. Vinoth .
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2021, 11 (03) :803-809
[9]  
Jebaseeli J., 2021, Journal of Engineering Research
[10]   Retinal Blood Vessel Segmentation from Depigmented Diabetic Retinopathy Images [J].
Jebaseeli, T. Jemima ;
Durai, C. Anand Deva ;
Peter, J. Dinesh .
IETE JOURNAL OF RESEARCH, 2021, 67 (02) :263-280