Beyond Internal Validation: Cross-Dataset Generalization of Machine Learning and Deep Learning Models for Oral Cancer Image Classification

The contribution of this study is the systematic evaluation of the cross-dataset generalization of oral cancer image classification models, moving beyond conventional within-dataset validation. By evaluating ML and DL models across two independently collected datasets using bidirectional external validation, the study quantifies the generalization gap and demonstrates that high internal performance may not translate to unseen datasets. The study further integrates the selected model into a web-based screening prototype, connecting model evaluation with practical accessibility.