Serum Raman spectroscopy combined with Gaussian—convolutional neural network models to quickly detect liver cancer patients
Chunzhi Meng, Hongyi Li, Chen Chen, Chen Chen, Wei Wu, Jing Gao, Yining Lai, Mila Ka, Min Zhu, Xiaoyi Lv, Fangfang Chen, Cheng Chen, Cheng Chen
Abstract
In recent years, liver cancer has caused great harm to human health. This study aims to use serum Raman spectroscopy combined with deep learning algorithms to classify liver cancer patients and control groups. For improving the robustness of models, we added equal proportions of Gaussian white noise of 5, 10, 15, 20, 25 dBW to enhance the data, and compared the results of convolutional neural networks and long short-term memory networks which show that the convolutional neural network combined with 10-fold data enhancement was better. The accuracy was 96.95%, indicating the great potential of this experimental model in liver cancer detection.