Open AccessImage X-Ray Classification for COVID-19 Detection Using GCLM-ELM
Author(s) -
Vivin Umrotul M. Maksum,
Dian Candra Rini Novitasari,
Abdulloh Hamid
Publication year - 2021
Publication title -
mantik
Language(s) - English
Resource type - Journals
eISSN - 2527-3167
pISSN - 2527-3159
DOI - 10.15642/mantik.2021.7.1.74-85
Subject(s) - extreme learning machine , artificial intelligence , preprocessor , computer science , feature extraction , feedforward neural network , pattern recognition (psychology) , artificial neural network , feature (linguistics) , linguistics , philosophy
COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs). The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.