Shaik Mahaboob Basha1,*, Victor Hugo C. de Albuquerque2, Samia Allaoua Chelloug3,*, Mohamed Abd Elaziz4,5,6,7, Shaik Hashmitha Mohisin8, Suhail Parvaze Pathan9
CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1981-2004, 2024, DOI:10.32604/cmes.2023.031425
Abstract Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images… More >
Graphic Abstract
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