An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm
Published:
Recommended citation: L. I. Mampitiya and N. Rathnayake, "An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm," 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, 2022, pp. 978-983, doi: 10.1109/MELECON53508.2022.9843023. https://ieeexplore.ieee.org/abstract/document/9843023
Abstract
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.
Recommended citation: L. I. Mampitiya and N. Rathnayake, “An Efficient Ocular Disease Recognition System Implementation using GLCM and LBP based Multilayer Perception Algorithm,” 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, 2022, pp. 978-983, doi: 10.1109/MELECON53508.2022.9843023.