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Dhruv Iyer¹, Kavin Gobinath², Krish Kowkuntla²

¹Mountain View High School, Mountain View, 94040, US.

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|November 25, 2025

Summary

This study presents an affordable electronic nose (e-nose) device for rapid, non-invasive lung cancer detection. The system achieved high accuracy, outperforming existing methods for early screening.

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Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

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Published on: March 9, 2018

Area of Science:

Biomedical Engineering
Analytical Chemistry
Oncology

Background:

Lung cancer is a leading cause of cancer mortality globally.
Non-invasive detection methods are crucial for early diagnosis.
Existing electronic nose (e-nose) systems for lung cancer detection have limitations in accuracy and speed.

Purpose of the Study:

To develop an affordable and accurate e-nose device for non-invasive lung cancer detection.
To improve the performance of e-nose systems by addressing limitations in accuracy and detection time.
To evaluate the efficacy of a novel data augmentation technique for enhancing e-nose analysis.

Main Methods:

Developed an affordable e-nose device with 12 metal oxide semiconductor sensors and 1 chemi-resistive alkane sensor, capable of detecting over 30 volatile organic compounds.
Collected breath samples from 28 healthy controls and 18 lung cancer patients.
Utilized a multilayer perceptron neural network for data analysis and employed Gaussian noise-based data augmentation to expand the dataset.
Performed 5-fold cross-validation for model evaluation.

Main Results:

The e-nose system achieved high diagnostic performance: 96.26% accuracy, 92.88% sensitivity, 97.75% specificity, and an AUC of 0.9286.
The developed system demonstrated a performance improvement of over 5% compared to existing e-nose detection methods.
Classification of breath samples was achieved in approximately 5 minutes.

Conclusions:

The developed e-nose system shows significant potential as a rapid, cost-effective tool for preliminary lung cancer screening.
The integration of data augmentation techniques enhances the accuracy and reliability of e-nose based diagnostics.
This technology offers a promising non-invasive approach to complement existing lung cancer diagnostic strategies.