Determination of Glaucoma Disease with Gray Level Co-occurrence Matrix Features

Authors : Evin ŞAHİN SADIK

Pages : 1-5

Doi:10.31590/ejosat.1202569

View : 21 | Download : 14

Publication Date : 2022-11-30

Article Type : Research Paper

Abstract :Glaucoma is a disease that causes an abnormal increase in intraocular pressure and therefore causes permanent damage to the optic nerves. Early and accurate diagnosis of the disease, known as the most \`insidious\` disease among eye diseases, is important. In this study, glaucoma prediction application was performed from high-resolution fundus photographs taken from an open-source database. Correlation, energy, homogeneity, contrast and entropy features were extracted from the segmented photographs using the gray-level co-occurrence matrix. Extracted features were divided into 66% test and 33% training after taking their average values. A 3-fold cross-validation was applied to the data and a feedback artificial neural network, classification and regression trees algorithm and k nearest neighbor algorithm were trained using 66% of the data. Classification success was also tested with 33% of test data. As a result, glaucoma and healthy individuals were classified with an average of 86.7% accuracy with the k nearest neighbor algorithm, an average of 87.8% accuracy with the decision trees, and an average of 96.7% accuracy with the artificial neural network algorithm. According to the results obtained, it was seen that glaucoma disease could be detected with high accuracy with the gray-level co-occurrence matrix features of glaucoma disease.
Keywords : Yapay Sinir Ağı, Göz Dibi Fotoğrafı, Sınıflandırma ve Regresyon Ağacı, Glokom, Gri seviye Eş oluşum matrisi, K en yakın komşuluk, Artificial Neural Network, Fundus, Classification and Regression Tree, Glaucoma, Gray level Co occurrence matrix, K nearest neighbor