# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset  
[PERSON] Win  
1 King Mongkut's Institute of Technology Ladrakang, School of Engineering, Bangkok 10520, Thailand; [EMAIL_ADDRESS] (K.Y.W.); [EMAIL_ADDRESS] (S.S.)1 King Mongkut's Institute of Technology Ladrakang, School of Engineering, Bangkok 10520, Thailand; [EMAIL_ADDRESS] (K.Y.W.  
However, it is a time-intensive and complex process, and not widely available in some geographic regions, especially in rural communities of developing countries. Medical images such as computed tomography (CT) scans and CXRs are vital for the early diagnosis and treatment of COVID-19 and represent the main clinical resources to evaluate the extent of lung damage caused by COVID-19 [3; 4]. COVID-19 patients exhibit several unique clinical and para-clinical features on radiology images, such as reciprocal, multifocal, ground-glass opacities with a fringe or back-dissemination in the early stage, and pulmonary consolidation in the late stage. Some studies have encountered changes in CXR and CT images before the onset of COVID-19 symptoms [5]. Although CT is a sensitive technique to diagnose COVID-19 and provides greater resolution, it is a cost-intensive and highly radiating technique that carries a high risk of cross-infection among medical experts and necessitates intensive sterilization of the apparatus [6]. In contrast to CTs, CXRs are less costly and inflict a lower radiation dose and are easier to obtain without risking contamination of the imaging equipment. The imaging process can be performed with portable X-ray machines at the patient's bedside. X-ray imaging machines are ubiquitously available worldwide and almost all hospitals have these machines; therefore, CXRs can be used to screen or triage COVID-19 without dedicated test kits, thus not incurring any overhead operational costs for data collection. Hence, CXR imagery has become a key tool to screen or triage COVID-19 patients and plays a vital role in the evaluation of lung deterioration incurred by COVID-19 [4; 7].  
The manual examination of CXRs is a time-intensive and subjective task and needs a high degree of expertise. There is a shortage of trained radiologists in many healthcare systems, especially in developing countries and remote areas. COVID-19 pneumonia may have similar pathology patterns with non-COVID-19 pneumonia; therefore, it is hard to discern COVID-19 CXRs from those of pneumonia, requiring extra time commitments and thereby exacerbating the shortage of competent medical experts. Moreover, COVID-19 has exhausted and caused a strain on medical experts [8]. Health services have been used to the fullest extent and sometimes to the brink of collapse due to the pandemic. These concerns necessitate the development of accessible and feasible innovative systems. With the advent of artificial intelligence (AI) and computer vision techniques, AI-assisted tools have become an adjunct tool for clinicians. They can serve as a complementary tool to human readings of medical images and aid the medical personnel in diagnoses. Thus, efficient and accurate AI-assisted tools are urgently needed to facilitate medical experts in COVID-19 diagnosis and expedite mass testing programs in high-risk areas, where many chest medical scans need to be examined. This study aimed to develop accurate and effective algorithms for the classification of COVID-19, normal, and pneumonia on CXRs using deep learning.  
Figure 1: Cumulative COVID-19 (**a**) confirmed cases and (**b**) the fatalities by territory and area [1] (accessed on 19 September 2020).