Classification of Oral Squamous Cell Carcinoma from Histopathological Images Using a Hybrid Deep Learning Model.

Background/Objectives: Oral squamous cell carcinoma (OSCC) has high mortality rates and leads to serious health problems when diagnosed late. This situation is considered a public health problem. Histopathological examination, which is an important point in the diagnosis of the disease, is a time-consuming and manual process that requires expertise. Methods: This study presents a deep learning-based approach for the automated classification of normal oral epithelium and oral squamous cell carcinoma (OSCC) from histopathological images. The performance of state-of-the-art architectures such as Vision Transformer (ViT), CLIP, ConvNextV2, Deit, and Dinov2 was comparatively analyzed. Based on the results, a hybrid architecture combining the strengths of the models is proposed. Results: In experiments conducted on a dataset of 696 histopathological images, the ConvNextV2 and ViTL16 architectures stood out among the basic models with accuracy rates around 84%. However, the most significant contribution of this study is the proposed method, which combines the global context capability of Transformer-based models with the local feature extraction power of CNN-based models using the Efficient Channel Attention (ECA)-Gated Features model. This hybrid model, created by integrating the ViTL16, Dinov2, and ConvNextV2 architectures, achieved 89.95% accuracy, 89.82% F1 score, and 89.95% sensitivity with a KNN classifier, outperforming the baseline models in the literature. Conclusions: The results obtained demonstrate that the fusion of multiple architectures increases diagnostic reliability in medical image analysis and can assist pathologists as a decision support mechanism.