Qualitative and Quantitative Transformer-CNN Algorithm Models for the Screening of Exhale Biomarkers of Early Lung Cancer Patients

Electronic nose (E-nose) has been applied many times for exhale biomarker detection for lung cancer, which is a leading cause of cancer-related mortality worldwide. These noninvasive breath testing techniques can be used for the early diagnosis of lung cancer patients and help improve their five year survival. However, there are still many key challenges to be addressed, including accurately identifying the kind of volatile organic compounds (VOCs) biomarkers in human-exhaled breath and the concentrations of these VOCs, which may vary at different stages of lung cancer. Recent research has mainly focused on E-nose based on a metal oxide semiconductor sensor array with proposed single gas qualitative and quantitative algorithms, but there are few breakthroughs in the detection of multielement gaseous mixtures. This work proposes two hybrid deep-learning models that combine the Transformer and CNN algorithms for the identification of VOC types and the quantification of their concentrations. The classification accuracy of the qualitative model reached 99.35%, precision reached 99.31%, recall was 99.00%, and kappa was 98.93%, which are all higher than those of the comparison algorithms, like AlexNet, MobileNetV3, etc. The quantitative model achieved an average R² of 0.999 and an average RMSE of only 0.109 on the mixed gases. Otherwise, the parameter count and FLOPs of only 0.7 and 50.28 M, respectively, of the model proposed in this work were much lower than those of the comparison models. The detailed experiments demonstrated the potential of our proposed models for screening patients with early stage lung cancer.