A deep learning framework for COVID-19 detection: integrating attention modules into ResNet50 and optimizing cross-entropy loss function

Abstract

Rapid and reliable diagnosis of COVID-19 is of fundamental importance for effective pandemic control, and chest X-ray imaging combined with deep learning provides a very useful tool for large-scale screening. Therefore, this paper presents a tri-class classification framework for distinguishing COVID-19, viral pneumonia, and normal cases from chest X-rays. The method is built on a pre-trained ResNet50 and incorporates CBAM attention modules at two well-justified stages: spatial attention in Layer3 for abnormal region localization and channel attention in Layer4 for semantic feature selection. More importantly, it addresses the class imbalance common in medical data by using a weighted cross-entropy loss function. Experiments on a public COVID-19 chest X-ray dataset demonstrate that the full model attains 90.74% precision and 74.65% F1-score, both superior to baseline methods. Ablation studies rigorously validate each component, and Precision-Recall curve analysis gives an AUC-PR of 0.942. From the results for the COVID-19 class it is clearly seen that the attention map visualization shows where the model is looking at clinically relevant lung regions, and since the inference time is 2.64 ms per image (379.1 FPS), the proposed framework thus achieves a good balance between accuracy and speed for clinical use.