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<p>Lung diseases, particularly lung cancer, pose significant global health challenges, leading to high morbidity and mortality rates. Early diagnosis is crucial for improving patient outcomes, yet traditional diagnostic methods often fall short in accuracy and timeliness. This study proposes a novel framework, named LDD-VTS, that uses Vision Transformers (ViTs) and SHAP (SHapley Additive exPlanations) to enhance the diagnosis and classification of lung diseases, including lung cancer, viral pneumonia, and lung opacity. The framework processes medical imaging data, such as chest X-rays and CT scans, to accurately identify abnormalities across multiple classes. The experimental results demonstrate that the P16-224-In21K model configuration achieves an impressive accuracy of 98.43% on the IQ-OTH/NCCD lung cancer dataset, alongside high precision and recall. Additionally, the integration of SHAP enhances interpretability, providing healthcare professionals with transparent insights into the model’s decision-making process. By identifying key image regions that influence predictions, the framework promotes trust and facilitates informed clinical decision-making. This study highlights the potential of AI-driven approaches to transform lung disease diagnostics, paving the way for improved early detection and personalized treatment strategies in clinical practice.</p>

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LDD-VTS: an AI-based framework for lung disease diagnosis using vision transformers and SHAP

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Hossam Magdy Balaha,
  • Rawan Ayman Ahmed,
  • Magdy Hassan Balaha

摘要

Lung diseases, particularly lung cancer, pose significant global health challenges, leading to high morbidity and mortality rates. Early diagnosis is crucial for improving patient outcomes, yet traditional diagnostic methods often fall short in accuracy and timeliness. This study proposes a novel framework, named LDD-VTS, that uses Vision Transformers (ViTs) and SHAP (SHapley Additive exPlanations) to enhance the diagnosis and classification of lung diseases, including lung cancer, viral pneumonia, and lung opacity. The framework processes medical imaging data, such as chest X-rays and CT scans, to accurately identify abnormalities across multiple classes. The experimental results demonstrate that the P16-224-In21K model configuration achieves an impressive accuracy of 98.43% on the IQ-OTH/NCCD lung cancer dataset, alongside high precision and recall. Additionally, the integration of SHAP enhances interpretability, providing healthcare professionals with transparent insights into the model’s decision-making process. By identifying key image regions that influence predictions, the framework promotes trust and facilitates informed clinical decision-making. This study highlights the potential of AI-driven approaches to transform lung disease diagnostics, paving the way for improved early detection and personalized treatment strategies in clinical practice.