Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data

Oral cancer is a highly aggressive disease that is often detected too late, resulting in poor survival outcomes. Screening methods are limited by subjective assessment and by the scarcity of confirmed cancer cases in real-world datasets. We developed a multimodal approach that combines photographic features with basic clinical information to improve early identification of malignant lesions while addressing the strong imbalance typically found in medical datasets. Multiple machine learning models were trained on a dataset of annotated oral images paired with patient metadata, using synthetic sample generation techniques to improve representation of cancer cases. Our best configuration achieved good macro F1 and Area Under Receiver Operating Characteristic, outperforming image-only baselines. The improvements indicate that integrating contextual patient information was associated with improved class-balanced performance under the evaluated protocol. These findings reflect performance differences under the evaluated multimodal protocol and imbalance learning for assisting clinicians in earlier recognition of oral cancer. Our key contribution lies in embedding-level oversampling within a fused image-metadata space, which preserves latent semantics while improving minority-class sensitivity. These results arise from a tightly controlled, single-center experimental setup and are best understood as exploratory insights into the methodology, rather than as evidence that can be readily extended to broader clinical contexts. These findings reflect performance differences within the evaluated experimental setting. Metadata integration is treated as a structural component of multimodal representation within a constrained experimental framework.