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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
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

2.0K

Advanced breath analysis through hierarchical deep convolutional neural network for multi-cancer screening.

Byeongju Lee1,2, Junyeong Lee1, Hyowoong Noh1

  • 1Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea.

NPJ Digital Medicine
|January 8, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a novel breath analysis method using a hierarchical deep convolutional neural network (HD-CNN) and a multimodal sensor array for early cancer detection. The platform successfully distinguished between healthy individuals, lung cancer, and gastric cancer patients with high accuracy.

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Respiratory Medicine

Background:

  • Breath analysis offers a noninvasive method for early cancer detection by identifying volatile organic compound (VOC) signatures.
  • Existing methods require further development for accurate multi-cancer classification.

Purpose of the Study:

  • To develop and validate a hierarchical deep convolutional neural network (HD-CNN) platform for dual-cancer classification using breath analysis.
  • To assess the performance of multimodal gas sensor arrays in distinguishing between healthy controls, lung cancer, and gastric cancer patients.

Main Methods:

  • Collected breath samples from 206 participants (67 healthy controls, 78 lung cancer, 61 gastric cancer).
  • Utilized a multimodal gas sensor array (SMO, EC, PID) to generate 2D response maps from time-resolved signals.

Related Experiment Videos

Last Updated: Jan 13, 2026

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

2.0K
  • Developed a two-stage HD-CNN model for coarse classification (healthy vs. cancer) and fine classification (lung vs. gastric cancer).

    • Main Results:

    • The HD-CNN achieved high classification accuracies: 82.1% (healthy), 84.0% (lung cancer), and 88.1% (gastric cancer).
    • Average AUCs were 0.89 (healthy), 0.92 (lung cancer), and 0.89 (gastric cancer).
    • The HD-CNN outperformed a 1D CNN, showing improved class separability and prediction confidence.

    Conclusions:

    • Hierarchical learning and multimodal sensing are effective for robust breath-based multi-cancer screening.
    • The developed HD-CNN platform demonstrates significant potential for early and accurate cancer detection through breath analysis.
    • Optimizing the hierarchical structure, particularly by first isolating healthy controls, enhances overall diagnostic performance.