Explainable Lung Disease Classification from Chest X-Ray Images
  Utilizing Deep Learning and XAI
Tanzina Taher Ifty Saleh Ahmed Shafin Shoeb Mohammad Shahriar Tashfia Towhid
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.