📝 SummarXiv

Abstract

SARS-CoV-2, the causative agent of COVID-19, remains a global health concern due to its high transmissibility and evolving variants. Although vaccination efforts and therapeutic advancements have mitigated disease severity, emerging mutations continue to challenge diagnostics and containment strategies. As of mid-February 2025, global test positivity has risen to 11%, marking the highest level in over six months despite widespread immunization efforts. Newer variants demonstrate enhanced host cell binding, increasing both infectivity and diagnostic complexity. This study evaluates the effectiveness of deep transfer learning in delivering rapid, accurate, and mutation-resilient COVID-19 diagnosis from medical imaging, with a focus on scalability and accessibility. We developed an automated detection system using state-of-the-art CNNs, including VGG16, ResNet50, ConvNetXtTiny, MobileNet, NASNetMobile, and DenseNet121 among others, to detect COVID-19 from chest X-ray and CT images. Among all the models evaluated, DenseNet121 emerged as the best-performing architecture for COVID-19 diagnosis using CT and X-ray images. It achieved an impressive accuracy of 98%, with 96.9% precision, 98.9% recall, 97.9% F1-score and 99.8% AUC score, indicating a high degree of consistency and reliability in both detecting positive and negative cases. The confusion matrix showed minimal false positives and false negatives, underscoring the model's robustness in real-world diagnostic scenarios.