A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images
Dilovan Asaad Zebari, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Merdin Shamal Salih
Abstract
Coronavirus (COVID-19) is a new contagious disease reasoned by a new virus that is widely spread over the world, this virus never has been identified in humans before. Respiratory disease can be affected by this virus such as flu with several symptoms, for example, fever, headache, cough, and pneumonia. COVID-19 presence in humans can be tested through blood samples or sputum while the result can be obtained in days. Further, biomedical image analysis assists in showing signs of pneumonia in a patient. Therefore, this paper aims to provide a fully automatic COVID-19 identification system by proposing a new fusion scheme of texture features for CT scan images. This paper presents a fusion scheme based on a machine learning system using three significant texture features, namely, Local Binary Pattern (LBP), Fractal Dimension (FD), and Grey Level Co-occurrence Matrices (GLCM). In experimental results, to demonstrate the efficiency of the proposed scheme we have collected 300 CT scan images from a publicly available database. The experimental result shows the performance of LBP, FD, and GLCM obtained an accuracy of 89.87%, 87.84%, and 90.98%, respectively while the proposed scheme yields better results by achieving 96.91% accuracy.