Deep learning-based classification of leukoplakia and OSCC using histopathological images: a performance evaluation study

Abstract

This thesis investigates the use of deep learning for histopathological classification of Oral Leukoplakia and Oral Squamous Cell Carcinoma (OSCC), two oral lesions that can be difficult to distinguish due to similar microscopic characteristics. Using the publicly available NDB-UFES dataset, six CNN architectures—ResNet-50, ResNet-101, DenseNet121, EfficientNet-B0, EfficientNet-B2, and ConvNeXt-Base—were comparatively evaluated under consistent experimental conditions. Performance was measured using Accuracy, Recall, AUROC, and MCC. ResNet-101 achieved the strongest overall performance, with 89.58% accuracy, 0.9481 AUROC, and 0.7810 MCC. Grad-CAM visualization further showed that the models focused on diagnostically relevant tissue regions. The findings demonstrate the potential of deep learning, particularly ResNet-101, as a foundation for AI-assisted oral cancer diagnosis.