Explainable Deep Learning for COVID-19 and Pneumonia Classification in Chest X-ray Images Using Layer-Wise Relevance Propagation

Abstract

Accurate and interpretable diagnosis of coronavirus disease 2019 (COVID-19) and pneumonia from chest X-ray images is critical for timely clinical decision-making, yet many deep learning models remain difficult to interpret in medical settings. In this study, a convolutional neural network (CNN) was developed to classify chest X-ray images into healthy, pneumonia, and COVID-19 categories, and its decision-making process was analyzed using explainable artificial intelligence (XAI) techniques. Layer-wise relevance propagation (LRP) and Deep Taylor Decomposition (DTD) were applied to generate attribution heatmaps identifying image regions most influential in model predictions, and multiple LRP variants were compared under both binary and multi-class classification settings. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Explainability was assessed through complementary qualitative and quantitative analyses: heatmaps from representative clinically characterized cases were compared with reported radiographic findings, while intersection over union (IoU) quantified spatial alignment between thresholded relevance maps and manually delineated lung-field regions of interest. The binary classifier achieved 95.4% accuracy, while the three-class model achieved 95.7% overall accuracy; COVID-19 classification yielded a precision of 0.988, recall of 0.982, and F1-score of 0.985. Qualitative analysis showed that several positive-attribution methods produced relevance patterns corresponding with radiographically described pulmonary abnormalities. Quantitative IoU analysis demonstrated substantial variation in anatomical lung-field alignment across XAI methods, with the highest observed IoU values reaching approximately 0.77 at a relevance threshold of t = 0.5. Together, these results highlight the importance of combining accurate classification with interpretable model explanations for more transparent and trustworthy medical imaging applications, particularly in rural and under-resourced healthcare settings.