Ensemble Learner for Covid-19 from Lung X-Ray Images

Abstract

Despite Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard of Covid-19 detection, some underdeveloped countries are lacking financially and suffer underdeveloped health system to perform fast Covid-19 detection. Both RT-PCR and Computed Tomography (CT) scan are costly diagnosis tool, thus computed diagnostic chest x-ray (CXR) is seen as fast and affordable option to perform Covid-19 diagnosis for underdeveloped countries. Despite of other works suggest to perform Local binary Pattern (LBP) and recent feature extraction methods such as Local Phase Quantization (LPQ), this works employed Gray-Level Co-Occurrence Matrix (GLCM) because it is a powerful method to extract textured features from gray-level images of chest x-ray. The learner to classify Covid-19 detection is tested via non tree-based learner such as k-Nearest Neighbour (kNN). This work also compared the performance especially in the tree-based and voting approach classifier. The experimentation shows that tree-based which uses voting and ensemble approach to detect Covid-19 from CXR images is a possible candidate learner to be improved for the underdeveloped countries.