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

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, Efficient-NetB2, 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 clin-icians in screening patients for COVID-19, thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients. Also, this paper can contri-bute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients. © 2023, Tech Science Press. All rights reserved.

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

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.