Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Early diagnosis of lung cancer, the leading cause of cancer-related death worldwide, is critical for reducing mortality rate. However, current diagnostic methods are invasive, time-consuming, costly, and may not always provide accurate diagnoses. For early diagnosis, recent research has focused on noninvasive approaches, including the detection of volatile organic compounds (VOCs) in human exhaled breath. Changes in the composition and concentration of VOCs in exhaled breath may indicate lung cancer, and this approach offers several advantages over traditional diagnostic methods. Moreover, the combination of a breath gas sensing system and machine learning algorithms provides a more accurate diagnosis. In this study, for the early diagnosis of lung cancer, a breath analysis system was developed using a gas sensor array and deep learning algorithm. The breath analysis system was designed to detect multiple VOCs in exhaled breath using ten semiconductor metal oxide (SMO), one photoionization detector (PID), nine electrochemical (EC) gas sensors. In total, 181 clinical breath samples (from 74 healthy controls and 107 lung cancer patients) were collected and analyzed using a 1D convolutional neural network (CNN) algorithm. The results showed an overall accuracy of 97.8% in classifying healthy controls and lung cancer patients using a complete clinical dataset. Through a comparison of the single-sensor type data and multimodal sensor data and performance analysis of three different deep learning models (multilayer perceptron, recurrent neural network, and CNN), we validated the potential of the breath analyzer with a multimodal sensor system and a 1D CNN as a lung cancer diagnostic device.