TY - JOUR ID - 155159 TI - Classification of COVID-19 on Chest X-Ray Images Through the Fusion of HOG and LPQ Feature Sets JO - Passer Journal of Basic and Applied Sciences JA - PSR LA - en SN - 27065944 AU - Hamaamin, Rebin Abdulkareem AU - Wady, Shakhawan Hares AU - Kareem, Ali Wahab AD - Applied Computer / Medicals and Applied Sciences / Charmo University AD - Applied Computer / Medicals and Applied Sciences / Charmo university AD - General Science / Education and Language / Charmo University Y1 - 2022 PY - 2022 VL - 4 IS - 2 SP - 135 EP - 143 KW - COVID-19 KW - CXR images KW - Feature Extraction KW - Machine Learning KW - Image classification DO - 10.24271/psr.2022.337896.1131 N2 - Covid-19 is a contagious disease that affects people's everyday life, personal health, as well as a nation's economy. COVID-19 infected individuals, according to a clinical study, are most usually contaminated with a severe condition after coming into a primary infection. The chest radiograph (also known as the chest X-ray or CXR) or a chest CT scan is a more reliable imaging method for diagnosing COVID-19 infected individuals. This article proposed a novel technique for classifying CXR scan images as healthy or affected COVID-19 by fusing the features extracted using Histogram of Oriented Gradient (HOG) and Local Phase Quantization (LPQ). This research is an experimental study that employed 7232 CXR images from a COVID-19 Radiography dataset as training and testing data. As a result, by using both individual and fused feature extraction methodologies, a developed model was created and fed into the machine learning techniques. The testing results reveal that the improved architecture outperforms current methods for identifying COVID-19 patients in terms of accuracy rate, which reached 97.15 %. UR - https://passer.garmian.edu.krd/article_155159.html L1 - https://passer.garmian.edu.krd/article_155159_6eedbbaab8bd06bf9ad961f7c5816c0e.pdf ER -