Deep Learning-Based Multiclass Classification of Oral Lesions with Stratified Augmentation

Abstract

Oral cancer is frequently diagnosed at later stages due to its similarity to other lesions. Existing research on computer-aided diagnosis has made progress using deep learning; however, most approaches remain limited by small, imbalanced datasets and a dependence on single-modality features, which restricts model generalization in real-world clinical settings. To address these limitations, this study proposes a novel data-augmentation–driven multimodal feature-fusion framework integrated within a (Vision Recognition)VR-assisted oral cancer recognition system. Our method combines extensive data-centric augmentation with fused clinical and image-based representations to enhance model robustness and reduce diagnostic ambiguity. Using a stratified training pipeline and an EfficientNetV2-B1 backbone, the system improves feature diversity, mitigates imbalance, and strengthens the learned multimodal embedding. Experimental evaluation demonstrates that the proposed framework achieves an overall accuracy of 82.57% on 2 classes, 65.13% on 3 classes, and 54.97% on 4 classes, outperforming traditional single-stream CNN models. These results highlight the effectiveness of multimodal feature fusion combined with strategic augmentation for reliable early oral cancer lesion recognition and serve as a foundation for immersive VR-based clinical decision-support tools.