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.
Abstract (translated)
肺疾病仍然是全球健康的一个关键问题，并且准确和快速诊断它们是非常重要的。这项工作重点对不同的肺疾病进行分类，分为五类：病毒性肺炎、细菌性肺炎、COVID-19、结核病和正常肺。利用先进的深度学习技术，我们探讨了包括CNN、混合模型、元学习、Transformer和Big Transfer在内的各种模型。研究包括全面的方法，如超参数调整、分层k-fold交叉验证和迁移学习中的微调。值得注意的是，我们的研究结果表明，通过5倍交叉验证进行微调的Xception模型具有最高的准确率，达到96.21%。这一成功表明，我们的方法在准确识别不同肺疾病方面非常有效。探索可解释人工智能（XAI）方法进一步增加了我们对这些模型决策过程的理解，有助于提高它们在临床应用中的信任度。
URL
https://arxiv.org/abs/2404.11428
