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Automated lung cancer classification using intensity-driven RoI selection and transfer learning.

Syed Thouheed Ahmed1, T Y Satheesha2, Lakshmi Hassan Nagaraja3

  • 1School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India. syedthouheed@reva.edu.in.

BMC Medical Informatics and Decision Making
|July 7, 2026
PubMed
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This study introduces an enhanced lung cancer classification framework using region-of-interest (RoI) selection and transfer learning. The novel approach achieves 97.84% accuracy, improving diagnostic prediction for lung cancer.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Computational Pathology

Background:

  • Accurate lung cancer diagnosis is challenged by expert variability and complex data integration from electronic health records and radiological images.
  • Extracting discriminative patterns for lung cancer classification from heterogeneous data sources remains a significant hurdle in clinical decision-making.

Purpose of the Study:

  • To propose an enhanced lung cancer classification framework utilizing intensity-driven region-of-interest (RoI) selection.
  • To improve the accuracy and robustness of lung cancer classification by refining annotations and optimizing feature extraction from medical imaging data.

Main Methods:

  • Leveraged intensity-driven RoI selection from LIDC-IDRI and TCIA datasets with customized label refinement for vulnerable regions.
Keywords:
CNN modelIntensity segmentationLung cancer classificationRoITransfer learningUDC-IDRI dataset

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  • Employed a high-dimensional RoI mapping strategy and a feedback-driven optimization mechanism within a transfer learning framework.
  • Utilized the CoVNet architecture for classification, evaluated with a 60:40 training-testing split.

    • Main Results:

    • Achieved a classification accuracy of 97.84% for lung cancer.
    • Demonstrated effective feature representation and discrimination through optimized RoI mapping and transfer learning.
    • Validated the framework's ability to enhance learning stability and predictive performance.

    Conclusions:

    • The proposed framework significantly improves lung cancer classification accuracy by integrating advanced RoI selection and transfer learning.
    • The methodology offers a robust approach for extracting clinically relevant features from radiological datasets, aiding in the identification of malignant nodules.
    • This work contributes to more reliable and efficient lung cancer diagnosis through AI-powered analysis of medical imaging.