Breath-based lung cancer detection using an ML-driven low-cost sensor array.

ABSTRACT: Lung cancer is the leading cause of cancer-related mortality worldwide. Lately, electronic nose (e-nose) systems have emerged as a promising method for non-invasive lung cancer detection. These systems, however, have several limitations, including low accuracy rates and long detection times. To address these challenges, we conducted a pilot study involving the development of an affordable e-nose device that can detect more than 30 volatile organic compounds, using twelve metal oxide semiconductor sensors and one chemi-resistive alkane sensor. The device recorded data for 28 healthy controls and 18 lung cancer breath samples that were then analyzed using a multilayer perceptron neural network. The dataset was expanded through a novel use of data augmentation, where Gaussian noise was applie

SUBMITTER: Iyer D 

PROVIDER: S-EPMC12644706 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature