Diagnosing the degree of differentiation between types of oral cancer based on extreme deep neural network model and Raman spectroscopy

Abstract

As a highly prevalent and recurrent cancer, detecting the degree of differentiation in oral cancer is crucial. Current methods rely on biopsies in the presence of significant lesions, which are time-consuming. This study introduces an efficient and rapid approach for mass determination of oral cancer differentiation levels. By leveraging serum Raman spectroscopy combined with deep neural networks and extreme gradient boosting, we performed feature selection and interaction on oral cancer samples of varying differentiation levels, achieving the most accurate and reliable classification of oral cancer differentiation stages.

Keywords:

• Deep learning
• feature intersection
• feature selection
• oral cancer
• Raman spectroscopy

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Additional information

Funding

This work was supported by the Distinguished Young Talents Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region (2022D01E11).