@Article{iasc.2023.025580,
AUTHOR = {Yassir Edrees Almalki, Maryam Zaffar, Muhammad Irfan, Mohammad Ali Abbas, Maida Khalid, K.S. Quraishi, Tariq Ali, Fahad Alshehri, Sharifa Khalid Alduraibi, Abdullah A. Asiri, Mohammad Abd Alkhalik Basha, Alaa Alduraibi, M.K. Saeed, Saifur Rahman},
TITLE = {A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {35},
YEAR = {2023},
NUMBER = {1},
PAGES = {163--180},
URL = {http://www.techscience.com/iasc/v35n1/48120},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2023.025580}
}