Open Access

ARTICLE

A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

Yassir Edrees Almalki¹, Maryam Zaffar^2,*, Muhammad Irfan³, Mohammad Ali Abbas², Maida Khalid², K.S. Quraishi⁴, Tariq Ali⁵, Fahad Alshehri⁶, Sharifa Khalid Alduraibi⁶, Abdullah A. Asiri⁷, Mohammad Abd Alkhalik Basha⁸, Alaa Alduraibi⁶, M.K. Saeed⁷, Saifur Rahman³

1 Department of Medicine, Division of Radiology, Medical College, Najran University, Najran, 11001, Saudi Arabia
2 Department of Computer Science and Information Technology, Ibadat International University, Islamabad, 44000, Pakistan
3 Electrical Engineering Department, College of Engineering, Najran University, Najran, 11001, Saudi Arabia
4 Department of Chemical Engineering, PIEAS University, Islamabad, 44000, Pakistan
5 Department of Computer Science, COMSATS University Islamabad (CUI), Sahiwal Campus, 57000, Pakistan
6 Department of Radiology, College of Medicine, Qassim University, Buraydah, 52571, Saudi Arabia
7 Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, 11001, Saudi Arabia
8 Faculty of Human Medicine, Zagazig University, 44631, Egypt

* Corresponding Author: Maryam Zaffar. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 163-180. https://doi.org/10.32604/iasc.2023.025580

Received 29 November 2021; Accepted 11 February 2022; Issue published 06 June 2022

Abstract

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients’ images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently. The performance of the Swin transformer is compared with the other seven deep learning models, including ResNet50, DenseNet121, InceptionV3, EfficientNetB2, VGG19, ViT, CaIT, Swim transformer provides 98% recall and 96% accuracy on corona affected images of the X-ray category. The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis, and proposed technique is found better in terms of accuracy. Our system could support clinicians in screening patients for COVID-19, thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients. Also, this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

---

Keywords

---