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Artificial Intelligence-Enhanced Optimization of Wireless Breath Sensor Arrays for Detection of Lung Cancer Using

Dong Dinh¹, Guojun Shang², Lei Cai^3,4

¹Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, New York 13902, United States.

|March 4, 2026

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This summary is machine-generated.

A new wireless breath sensor platform uses artificial intelligence to detect lung cancer early. This non-invasive tool analyzes volatile organic compounds (VOCs) for improved patient survival and accessible screening.

Area of Science:

Biomedical Engineering
Nanotechnology
Artificial Intelligence

Background:

Early lung cancer detection is vital for patient survival but current methods are costly and inaccessible.
Non-invasive screening methods are needed to improve accessibility and reduce healthcare burdens.

Purpose of the Study:

To develop and validate a fully integrated wireless breath sensing platform for early lung cancer detection.
To utilize AI-driven optimization for enhanced volatile organic compound (VOC) detection in breath samples.

Main Methods:

Developed a wireless platform combining nanostructured chemiresistive (NC) sensor arrays with an AI-driven Fuzzy logic-guided Genetic Algorithm (Fuzzy-GA).
Collected breath samples from lung cancer patients (n=35) and non-cancer participants (n=47).

Keywords:

breath sensor array system fuzzy-GA optimization lung cancer detection machine learning classification non-invasive screening volatile organic compounds wireless monitoring

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Applied supervised machine learning models (KNN, SVM, Random Forest, XGBoost, CNN) to analyze VOC patterns.

Main Results:

The Fuzzy-GA algorithm optimized sensor array size while maintaining high diagnostic performance.
The optimized platform achieved up to 96% classification accuracy in distinguishing lung cancer patients from controls.
Demonstrated reduced system complexity, lower cost, and improved scalability for real-world deployment.

Conclusions:

The developed wireless breath sensing platform offers a clinically viable, non-invasive tool for early lung cancer detection.
The AI-driven optimization significantly enhances diagnostic performance and system efficiency.
The platform holds potential for at-home monitoring and broader disease screening applications.