MGFFCNN: Two‐dimensional matrix spectroscopy combined with multi‐channel gradient feature fusion convolutional neural network means to diagnose glioma and esophageal cancer patients

Abstract

AbstractCurrently, glioma and esophageal cancer are common malignant tumors worldwide with low cure rate and high mortality rate, and they endanger human health seriously. In this study, we analyzed the correlation and difference between glioma and esophageal cancer through serum mid‐infrared and Raman spectra and established a multi‐channel gradient feature fusion convolutional neural network to achieve rapid diagnosis of glioma and esophageal cancer patients. We transformed the spectra from one‐dimensional matrix to two‐dimensional matrix form separately as the input of the network and fused the features extracted from the Flatten layer of the network. First, we fused the features of mid‐infrared and Raman spectra and constructed a two‐channel gradient feature fusion idea. Then, in order to enrich the learning of features further, we took the first‐order derivative of mid‐infrared and Raman original spectra, respectively, and used the derivative spectra as two channels as well. The mid‐infrared and Raman spectra in two‐dimensional matrix form were fused with their derivative spectral features, respectively, and the fused features were fused again to construct a four‐channel gradient feature fusion network model. Finally, compared with the single original spectrum and the one‐dimensional matrix feature fusion spectrum, the two‐dimensional matrix feature fused spectrum was more advantageous, and the classification accuracy of the model was as high as 99.2% ± 0.7%. This study showed that two‐dimensional matrix spectra combined with multi‐channel gradient feature fusion technique had great potential for rapid and accurate identification of patients with glioma and esophageal cancer.