Oral Cancer Detection By Using Tabular Data Synthesis and Classification.

Abstract

We advance the state of the art in AI-driven oral cancer screening approaches through a new automatic method for analysis of non-image clinical data organized in a tabular format. In this work, we distinguish oral cancer from precancer (which is its direct precursor) and improve the classification performance by mitigating the issue of clinical data imbalance. Specifically, our work includes two main components: classification using the tabular data, and tabular data synthesis for data balancing and improving classification. We use deep learning techniques in both components and conduct extensive experiments by creating multiple data sets and evaluating several models. Our work indicates that the tabular clinical information can play an important role for identifying oral cancer from precancer using deep learning techniques. The Youden index of our approach is ~0.74, the balanced accuracy is ~0.83, and sensitivity is ~0.90. Our work demonstrates that using synthetic data to balance the tabular data sets is a promising approach for improving the classification performance, and the improvement is statistically significant (p-value < 0.05) with respect to the evaluation metrics: sensitivity, Youden index, F1 score, balanced accuracy, and Matthews correlation coefficient (MCC). Broadly, the encouraging results and insights obtained through our work can help motivate new research utilizing clinical tabular data toward improving clinical AI prediction tasks.