Deep learning methods for oral cancer detection using Raman spectroscopy

The Raman spectroscopy analysis has been applied to the detection and research of oral cancer. One of the essential works in this technique is the Raman spectral data analysis method, which is mainly divided into two categories: traditional machine learning and deep learning. Especially, the deep learning method is proved that it could obtain higher accuracy in oral cancer identification than the traditional machine learning method. The purpose of this study is to test, compare, and analyze the performance of existing classical deep neural network models (AlexNet, VGGNet, ResNet50, MobileNetV2, Transformer) that recognize multiple types of oral cancer tissues. To achieve this goal, 16,200 Raman spectra are first collected from 180 tissue samples of 90 patients who have undergone a surgical resection due to tongue squamous cell carcinoma, gingival squamous cell carcinoma, and buccal squamous cell carcinoma. Then, the models are trained and predicted at the patient level. The experimental results demonstrate that the ResNet50 has the best performance in the identification of oral cancer tissue and normal tissue with an overall accuracy rate of 92.81%, an overall precision rate of 92.93%, and an overall recall rate of 92.86%. With this foundation, we further develop a prototype intelligent detection system with above five classical deep neural network models to realize multi-types of oral cancer tissue detection. Hopefully, our work can provide a guide for oral cancer detection using the deep learning method with Raman spectroscopy analysis and promote the development of clinical diagnosis system for oral cancer. The code is available at https://github.com/ISCLab-Bistu/deep-learning-for-OSCC.