Open AccessFuzzy modeling network type 2 and principal component analysis for the diagnosis of diabetic retinopathy
Author(s) -
Auli Damayanti,
Siti Maimunah,
Asri Bekti Pratiwi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1306/1/012020
Subject(s) - diabetic retinopathy , fundus (uterus) , backpropagation , artificial intelligence , retinopathy , computer science , principal component analysis , fuzzy logic , pattern recognition (psychology) , medicine , adaptive histogram equalization , grayscale , diabetes mellitus , artificial neural network , histogram equalization , image processing , ophthalmology , image (mathematics) , endocrinology
Diabetic retinopathy is a disease caused by vascular complications of diabetes mellitus. The more increasing number of people with diabetes mellitus every year, then indirectly the chances of someone’s eye experiencing diabetic retinopathy disorders are also increasing. Fundus photos are one way to detect diabetes mellitus in the retina of the eye. The stages used in the detection process of diabetic retinopathy include the stage of pre-processing fundus images namely grayscale and histogram equalization processes, the stage of reducing image size using Principal Components Analysis (PCA) and the stage of diabetic retinopathy detection on fundus images using fuzzy modeling network type 2. Fuzzy modeling network type 2 is a method using multilayer neural network architecture with backpropagation learning and fuzzy systems for its rules. The results of the validation system test show that the process of detecting diabetic retinopathy using the type 2 fuzzy modeling network algorithm is obtained the accuracy of 80%.