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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
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A deep learning model to enhance lung cancer detection using 'Dual-Branch' model classification approach.

Emad Shweikeh1, Murad Al-Rajab2, Joan Lu3

  • 1Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.

Plos One
|January 9, 2026
PubMed
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This study introduces the Dual-Branch Model Classification Approach (DbMCA) for lung cancer detection, improving accuracy by integrating image and mask data. The DbMCA significantly enhances diagnostic capabilities for lung cancer, outperforming baseline models.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Lung cancer is a leading cause of cancer-related deaths globally.
  • Computed tomography (CT) is crucial for lung cancer diagnosis, but challenges like limited data and input modalities persist.
  • Accurate lung cancer classification is vital for improving patient survival rates.

Purpose of the Study:

  • To introduce and evaluate the Dual-Branch Model Classification Approach (DbMCA) for enhanced lung cancer detection.
  • To assess the impact of sample size and dual-input modalities (image and mask) on diagnostic model performance.
  • To address limitations in existing lung cancer detection methods, such as data scarcity and single-modality inputs.

Main Methods:

  • Developed a two-stage Dual-Branch Model Classification Approach (DbMCA).

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  • Integrated both image and mask data for a multi-modal classification strategy.
  • Conducted comparative experiments on the LIDC-IDRI dataset with varying sample sizes.

    • Main Results:

    • DbMCA achieved 91.21% accuracy and 91.18% F1-score on smaller datasets, and 98.04% accuracy and 98.01% F1-score on larger datasets.
    • Demonstrated superior performance compared to baseline models, especially with integrated multi-modal information.
    • Highlighted the sensitivity of Convolutional Neural Network (CNN) performance to sparse mask data, with Deep Neural Networks (DNN) and Support Vector Machines (SVM) showing better scalability.

    Conclusions:

    • The DbMCA significantly improves lung cancer detection accuracy and scalability by integrating multi-modal data.
    • The model shows potential for detecting subtle lung cancer patterns, outperforming weaker baselines.
    • Future work should focus on enhancing image quality, expanding datasets, and addressing segmentation constraints for better generalization.