Open AccessPre-Diabetic Retinopathy identification using hybridGenetic Algorithm-Neural Network classifier
Author(s) -
P Mohamed Jebran,
Shweta Gupta
Publication year - 2021
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 - 10.1088/1742-6596/1937/1/012033
Subject(s) - computer science , diabetic retinopathy , artificial intelligence , artificial neural network , classifier (uml) , feature extraction , algorithm , receiver operating characteristic , pattern recognition (psychology) , diabetes mellitus , machine learning , medicine , endocrinology
Diabetic retinopathy (DR) is one of the main prevalent diabetes problems, causing blurry vision and degeneration amongst adults of working age. The first symptoms of DR include Microaneurysms (MA). A Genetic Algorithm-Artificial Neural Network (GA-NN) technique is developed for early diagnosis of DR. There are five steps of the proposed framework. Image pre-processing is achieved using r-polynomial transformation. In the extraction, the K-means algorithm is used to segment blood vessels, and candidate patches were generated. Shape attributes, GLCM and LBP features have been derived from excluded blood vessel image and from patches separately. To achieve independent classification, GA-NN classifiers is employed. The ultimate decision system projects the MA or non-MA class labels by plurality voting for eachclassifier. This methodology was tested on two databases: e-Ophtha-MA and DIARETDB1. The e-ophtha-MA and DIARETDB1 datasets had AUCs of 0.89 and 0.87, respectively, on the receiver operating characteristic (ROC) curve.