Comparative Evaluation of Deep Learning Models for Diagnosis of COVID-19 Using X-ray Images and Computed Tomography

(1) Background: The COVID-19 pandemic is an unprecedented global challenge, having affected more than 776.79 million people, with over 7.07 million deaths recorded since 2020. The application of Deep Learning (DL) in diagnosing COVID-19 through chest X-rays and computed tomography (CXR and CT) has proven promising. While CNNs have been effective, models such as the Vision Transformer and Swin Transformer have emerged as promising solutions in this field. (2) Methods: This study investigated the performance of models like ResNet50, Vision Transformer, and Swin Transformer. We utilized Bayesian Optimization (BO) in the diagnosis of COVID-19 in CXR and CT based on four distinct datasets: COVID-QU-Ex, HCV-UFPR-COVID-19, HUST-19, and SARS-COV-2 Ct-Scan Dataset. We found that, although all tested models achieved commendable performance metrics, the Swin Transformer stood out. Its unique architecture provided greater generalization power, especially in cross-dataset evaluation (CDE) tasks, where it was trained on one dataset and tested on another. (3) Results: Our approach aligns with state-of-the-art (SOTA) methods, even in complex tasks like CDE. On some datasets, we achieved exceptional performance metrics, with AUC, Accuracy, Precision, Recall, and F1-Score values of 1. (4) Conclusion: Results obtained by the Swin Transformer go beyond what is offered by current SOTA methods and indicate actual feasibility for application in medical diagnostic scenarios. The robustness and generalization power of the Swin Transformer, demonstrated across different datasets, encourage future exploration and adoption of this approach in clinical settings.