Deep Transfer Learning for Automated Oral Cancer Classification from Clinical Images: Controlled Comparative Evaluation and External Validation

Deep transfer learning has been used to classify oral cancer in clinical photographs. Most comparisons depend on a single training run and data split, and few control for leakage or test on external data. In this study, 2,444 images from five public repositories were audited using perceptual hashing and deep-feature similarity. Duplicates and label conflicts accounted for 57.2% of files, and 1,045 images were retained. Seven ImageNet-pretrained convolutional neural networks were trained with three seeds each under a common two-stage protocol. Checkpoints and a threshold of 0.5 were fixed on validation data before the models were evaluated once on an internal test set and on an independent clinical cohort (813 images; 303 patients). Mean internal areas under the ROC curve (AUCs) were between 0.951 and 0.972, and after Holm adjustment no model differed significantly from ResNet50. On the external cohort, ResNet50 retained moderate discrimination (AUC 0.855; 95% confidence interval, 0.808–0.893), whereas its specificity declined from 0.887 to 0.320. A threshold derived from 91 external patients increased specificity on the remaining 212 patients to 0.756 (sensitivity 0.790). Evaluation design had a larger effect than the choice of architecture. Leakage auditing, locked external validation and local recalibration should precede clinical application.