Evaluation of Machine Learning Methods for Oral Cavity Histopathological Cancer Classification in a Brazilian Cohort - Baffa, Matheus de Freitas Oliveira; Bachmann, Luciano; Zezell, Denise Maria; Matos, Leandro Luongo; Felipe, Joaquim Cezar (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.20718, 2026)
Histopathological evaluation is the gold standard for oral cavity cancer diagnosis, but it is time-consuming and subject to inter-observer variability. In this study, we compare radiomics, convolutional neural networks, and a DINOv2-based transformer model for histopathological image classification using a Brazilian cohort. Radiomic features combined with a fully-connected neural network achieved the best overall performance, reaching 93.18% accuracy, 95.56% sensitivity, and 92.50% specificity, while requiring lower computational cost than end-to-end deep learning models. These findings suggest that radiomics provides an efficient alternative for oral cavity cancer classification, particularly in scenarios with limited data and computational resources.