Open AccessSemantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks
Author(s) -
Ričardas Toliušis,
Olga Kurasova,
Jolita Bernatavičienė
Publication year - 2020
Publication title -
informacijos mokslai
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.128
H-Index - 1
eISSN - 1392-1487
pISSN - 1392-0561
DOI - 10.15388/im.2020.90.53
Subject(s) - convolutional neural network , computer science , segmentation , artificial intelligence , fundus (uterus) , glaucoma , diabetic retinopathy , computer vision , macular degeneration , pattern recognition (psychology) , ophthalmology , medicine , diabetes mellitus , endocrinology
The article reviews the problems of eye bottom fundus analysis and semantic segmentation algorithms used to distinguish eye vessels, optical disk. Various diseases, such as glaucoma, hypertension, diabetic retinopathy, macular degeneration, etc., can be diagnosed by changes and anomalies of vesssels and optical disk. For semantic segmentation convolutional neural networks, especially U-Net architecture, are well suited. Recently a number of U-Net modifications have been developed that deliver excellent performance results.