Rapid diagnosis of lung cancer and glioma based on serum Raman spectroscopy combined with deep learning

Abstract Lung cancer and glioma are common malignancies worldwide and pose a serious threat to human health. There may be a certain correlation between lung cancer patients and glioma patients in serum composition, but to date, no study on the classification and correlation of lung cancer and glioma is available. In this paper, the differences and relationships between lung cancer and glioma were analyzed from serum Raman spectra. The existing detection methods of lung cancer and glioma are time consuming and expensive, so we propose a method based on patient serum Raman spectra combined with deep learning, which can screen lung cancer and glioma accurately with speed and low cost. In this study, features were extracted from the original spectral data of patients with lung cancer, glioma, and control subjects. By adding different decibels of white Gaussian noise to the training set for data enhancement, the enhanced training set data were imported into a multilayer perceptron (MLP), recursive neural network (RNN), convolutional neural network (CNN), and AlexNet using fivefold cross‐validation to build the diagnostic model. The results show that PLS‐AlexNet is the best model. The accuracy of this model in the binary classification experiment of lung cancer and control subjects, lung cancer and glioma, and glioma and control subjects were 99%, 95.2%, and 100%, respectively, and the experimental accuracy of the AlexNet triclassification algorithm is also above 85%. This method has great potential in clinical diagnosis of diseases.