ORIGINAL ARTICLE Automated COVID-19 detection in chest X-ray images using fine-tuned deep learning architectures Sonam Aggarwal 1 | Sheifali Gupta 1 | Adi Alhudhaif 2 | Deepika Koundal 3 | Rupesh Gupta 1 | Kemal Polat 4 1 Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India 2 Department of Computer Science, College of Computer Engineering and Sciences in Al- kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia 3 Department of Virtualization, School of Computer Science, Dehradun, Uttarakhand, India 4 Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey Correspondence Adi Alhudhaif, Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia. Email: a.alhudhaif@psau.edu.sa Abstract The COVID-19 pandemic has a significant impact on human health globally. The ill- ness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breath- lessness, fever, and other symptoms. Although, the reverse transcription polymerase- chain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used. Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumo- nia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%. KEYWORDS artificial intelligence, chest X-rays, COVID-19, deep learning, transfer learning 1 | INTRODUCTION The year 2020 was marked by a continuing pandemic that was first detected in Wuhan, China, at the end of December 2019 (Wang et al., 2020). As recorded on 18th July 2020, there have been 1,40,60,402 infected cases causing 6,01,820 deaths worldwide (Worldometer, 2020). COVID-19 is caused by a coronavirus named SARS-Cov-2, which belongs to the β-coronavirus family. This virus is highly transmissible compared to viruses causing Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), namely MERS-Cov and SARS-Cov. Analysis of Received: 12 April 2021 Revised: 9 May 2021 Accepted: 23 May 2021 DOI: 10.1111/exsy.12749 Expert Systems. 2021;e12749. wileyonlinelibrary.com/journal/exsy © 2021 John Wiley & Sons Ltd. 1 of 17 https://doi.org/10.1111/exsy.12749