Open AccessEnsemble Learner for Covid-19 from Lung X-Ray Images
Author(s) -
Yasmin binti Mohd Yacob,
Rafikha Aliana A. Raof,
Phak Len Eh Kan,
Norsuhaida Ahmad,
S. W. M. Ismail
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1878/1/012060
Subject(s) - local binary patterns , computer science , majority rule , artificial intelligence , covid-19 , classifier (uml) , voting , pattern recognition (psychology) , medicine , image (mathematics) , pathology , histogram , disease , politics , political science , infectious disease (medical specialty) , law
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.