International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 4, August 2022, pp. 3655~3664 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i4.pp3655-3664  3655 Journal homepage: http://ijece.iaescore.com A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images Ahmmad Musha 1 , Abdullah Al Mamun 2 , Anik Tahabilder 3 , Md. Jakir Hossen 2 , Busrat Jahan 4 , Sima Ranjbari 5 1 Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna, Bangladesh 2 Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia 3 Department of Computer Science, Wayne State University, Detroit, United States 4 Department of Computer Science and Engineering, Feni University, Feni, Bangladesh 5 School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran Article Info ABSTRACT Article history: Received Oct 14, 2020 Revised Apr 9, 2022 Accepted Apr 19, 2022 There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life. Keywords: Coronavirus COVID-19 Deep learning Pneumonia X-ray images This is an open access article under the CC BY-SA license. Corresponding Author: Abdullah Al Mamun, Md. Jakir Hossen Faculty of Engineering and Technology, Multimedia University Ayer Keroh, Melaka-7540, Malaysia Email: mamun130203@gmail.com, jakir.hossen@mmu.edu.my 1. INTRODUCTION Coronaviruses (CoVs) are a larger family of harmful viruses that can affect humans and other animals and even can cause death. In the 21st century, two widely zoonotic CoVs, Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV), spread from animal reservoirs to cause global pandemics with alarming morbidity and mortality [1]. Recently, coronavirus disease 2019 (COVID-19), which is owing to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been emerged in Wuhan, China, in December 2019 [2]. It has spread out all over the world and affected 307 M people, and results in 5.5 M deaths all over the world till Jan 09, 2022 [3]. COVID-19 epidemic was certified as a global pandemic on March 11, 2020, by the World Health Organization [4].