An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm

• Published in: IEEE Mediterranean Electrotechnical Conference pp. 978 - 983
• Main Authors: Mampitiya, Lakindu Induwara, Rathnayake, Namal
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 14.06.2022
• Subjects: Cataracts; Classification; Conferences; Feature extraction; Fundus; GLCM; Hypertension; LBP; MLP; Neural networks; Nonhomogeneous media; Ocular; Pathology
• ISSN: 2158-8481
• DOI: 10.1109/MELECON53508.2022.9843023

This research study is focused on the classification of ocular diseases by referring to a well-known dataset. The data is divided into seven classes: diabetes, glaucoma, cataract, normal, hypertension, age-related macular degeneration, pathological myopia, and other diseases/abnormalities. A Neural Network is used for the classification of diseases. In addition, the GLCM and LBP feature extracting methods have been used to carry out the feature extraction for the fundus images. This study compares five different ocular disease recognizing techniques. Moreover, the proposed model was evaluated regarding precision, recall, and accuracy. The proposed solution outperformed existing state-of-the-art algorithms, achieving 99.58% accuracy.