Automated Grading of Oral Squamous Cell Carcinoma into Multiple Classes Using Deep Learning Methods

Abstract

The diagnosis of oral squamous cell carcinoma is based on a histopathological examination, which is still the most reliable way of identifying oral cancer despite its high subjectivity. However, due to the heterogeneous structure and textures of oral cancer, as well as the presence of any inflammatory tissue reaction, histopathological classification can be difficult. For that reason, an automatic classification of histopathology images with the help of artificial intelligence-assisted technologies can not only improve objective diagnostic results for the clinician but also provide extensive texture analysis to get a correct diagnosis. In this paper various deep learning methods are compared in order to get an AI-based model for multiclass grading of OSCC with the highest $\mathbf{AUC}_{\mathbf{micro}}$ and ${\text{AUC}}_{\text{macro}}$ values.