Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913318710542336 |
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| author | Ifty, Tanzina Taher Shafin, Saleh Ahmed Shahriar, Shoeb Mohammad Towhid, Tashfia |
| author_facet | Ifty, Tanzina Taher Shafin, Saleh Ahmed Shahriar, Shoeb Mohammad Towhid, Tashfia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI Ifty, Tanzina Taher Shafin, Saleh Ahmed Shahriar, Shoeb Mohammad Towhid, Tashfia Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.11428 |