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Optimizing Early Lung Cancer Detection on Chest Radiographs: AI-Based Lung Segmentation for Enhanced Diagnostic Accuracy

  • Unique Paper ID: 181902
  • Volume: 12
  • Issue: 2
  • PageNo: 98-102
  • Abstract:
  • Early detection of lung cancer significantly improves survival rates, yet interpreting chest radiographs (CXR) remains challenging due to overlapping anatomical structures. This study proposes an AI-based segmentation framework using U-Net to isolate lung regions, enhancing the performance of subsequent nodule detection models. We trained and evaluated the model on publicly available Montgomery and Shenzhen datasets, achieving a Dice coefficient of 0.961 and IoU of 0.924. Further classification on the JSRT dataset demonstrated improved sensitivity and specificity in nodule detection when lung segmentation was applied. This work affirms that AI-based lung segmentation significantly improves early lung cancer diagnostic accuracy and provides a foundation for scalable computer-aided diagnosis systems.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 2
  • PageNo: 98-102

Optimizing Early Lung Cancer Detection on Chest Radiographs: AI-Based Lung Segmentation for Enhanced Diagnostic Accuracy

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Journal no 47859

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