Automatic Classification of Coronavirus Disease-19 Chest X-Ray Images Using Local Binary Pattern and Binary Particle Swarm Optimization for Feature Selection

Novel Coronavirus disease 2019 (COVID-19) is a type of pandemic viruses that causes respiratory tract infection in humans. The clinical
imaging of Chest X-Ray (CXR) by Computer-Aided Diagnosis (CAD) plays an essential role in identifying the patients infected by
COVID-19. The objective of this paper presents a CAD method for automatically classifying 110 frontal CXR images of contagious people
according to Normal and COVID-19 infection. The proposed method contains four phases: Image enhancement, feature extraction,
feature selection, and classification. Gaussian filter is performed to de-noise the images and Adaptive Histogram Equalization for image
enhancement in the pre-processing step for better decision-making. Local Binary Pattern features set are extracted from the dataset.
Binary Particle Swarm Optimization is considered to select the clinically relevant features and develop a robust model. The successive
features are fed to Support Vector Machine and K-Nearest Neighbor classifiers. The experimental results show that the system robustness
in classification COVID-19 from normal images with average accuracy 94.6%, sensitivity 96.2%, and specificity 93%.