Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI

• URL: http://arxiv.org/abs/2404.11428v1
• Date: Wed, 17 Apr 2024 14:34:35 GMT
• Title: Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI
• Authors: Tanzina Taher Ifty, Saleh Ahmed Shafin, Shoeb Mohammad Shahriar, Tashfia Towhid,
• Abstract summary: This work focuses on classifying different lung diseases into five groups: viral pneumonia, bacterial pneumonia, COVID, tuberculosis, and normal lungs. We explore a diverse range of models including CNN, hybrid models, ensembles, transformers, and Big Transfer. Remarkably, the Xception model, fine-tuned through 5-fold cross-validation, achieves the highest accuracy of 96.21%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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