Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI
This work addresses the need for accurate and quick diagnosis of lung diseases, which is a critical global health concern, but it appears incremental as it applies existing deep learning methods to a specific medical domain.
The paper tackled the problem of classifying lung diseases from chest X-ray images into five categories, achieving a highest accuracy of 96.21% using a fine-tuned Xception model.
Lung diseases remain a critical global health concern, and it's crucial to have accurate and quick ways to diagnose them. This work focuses on classifying different lung diseases into five groups: viral pneumonia, bacterial pneumonia, COVID, tuberculosis, and normal lungs. Employing advanced deep learning techniques, we explore a diverse range of models including CNN, hybrid models, ensembles, transformers, and Big Transfer. The research encompasses comprehensive methodologies such as hyperparameter tuning, stratified k-fold cross-validation, and transfer learning with fine-tuning.Remarkably, our findings reveal that the Xception model, fine-tuned through 5-fold cross-validation, achieves the highest accuracy of 96.21\%. This success shows that our methods work well in accurately identifying different lung diseases. The exploration of explainable artificial intelligence (XAI) methodologies further enhances our understanding of the decision-making processes employed by these models, contributing to increased trust in their clinical applications.