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A hybrid AI method for lung cancer classification using explainable AI techniques.

Resham Raj Shivwanshi1, Neelam Shobha Nirala1

  • 1Department of Biomedical Engineering, National Institute of Technology Raipur, 492010, India.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
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Summary

This study introduces a hybrid AI model for lung cancer detection in CT scans, achieving high accuracy. Explainable AI helps understand model decisions for improved computer-assisted diagnosis systems.

Keywords:
CATBOOST classifierExplainable AI (XAI)Lung cancer CADRadiomic featuresReliable cancer assessmentVision transformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Computer-assisted diagnosis (CAD) systems for lung cancer (LC) analysis of CT images face challenges due to complex image structures and abnormality locations.
  • Extracting relevant information for LC CAD systems is difficult, hindering effective model development.

Purpose of the Study:

  • To present a hybrid AI method for lung cancer malignancy classification to aid researchers in engineering model performance.
  • To improve the decision-making process of LC CAD systems through explainable AI.

Main Methods:

  • The IncCat-LCC: Explainer methodology combines handcrafted radiomic features (HcRdF), InceptionNet CNN features (INCF), and Vision Transformer features (ViTF) for extraction.
  • Feature selection is performed using XGBOOST (XGB), followed by GPU-based CATBOOST (CB) classification.

Main Results:

  • The framework achieved high performance in lung nodule multiclass malignancy classification.
  • Key metrics include accuracy (96.74%), precision (93.68%), recall (96.74%), F1-score (95.19%), specificity (98.47%), and AUC (99.76%) for the normal class.

Conclusions:

  • Explainable AI (XAI) provides insights into model performance and statistical outcomes, aiding reader comprehension.
  • This work can enhance existing LC CAD systems by using XAI to identify factors contributing to improved performance and reliability.