A Statistically Validated and Explainable Deep Learning Framework for Oral Cancer Classification.

Authors

Nada Mohamed, Muhammad H Zayyan, Sara El-Metwally

Published in

Journal of imaging informatics in medicine. Oct 09, 2026. Epub Oct 09, 2026.

Abstract

Despite numerous deep learning models reporting high accuracy for oral cancer classification, most rely on a single train/test split on a single dataset, with limited statistical validation, external generalization testing, or explainability. This study addresses that gap through a statistically validated, externally tested, and dual-explainable classification framework. We provide a rigorous benchmarking study contrasting 20 hybrid combinations (4 pre-trained backbones × 5 classical classifiers) against fine-tuned end-to-end CNNs. Across repeated stratified fivefold cross-validation with 10 repetitions, the hybrid framework consistently outperformed end-to-end CNNs, with ResNet50 paired with an SVM-RBF classifier achieving top performance: 0.960 accuracy (95% CI 0.958-0.963), 0.953 F1-score (95% CI 0.951-0.956), and 0.991 ROC-AUC (95% CI 0.990-0.993), with Wilcoxon signed-rank tests confirming statistical significance (p < 0.001). MobileNetV2 paired with SVM-RBF emerged as an efficient lightweight alternative, suited to resource-constrained point-of-care settings. On an independent external validation dataset, the optimal ResNet50 + SVM-RBF model demonstrated robust generalization, achieving 0.916 accuracy (95% CI 0.892-0.939), 0.984 ROC-AUC (95% CI 0.975-0.992), and 0.98 precision (95% CI 0.965-1.000), indicating a low false-positive rate on unseen samples. To ensure clinical trust, a dual Explainable AI (XAI) framework combining Grad-CAM and SHAP was implemented, providing localized visual heatmaps and global feature attribution. The proposed pipeline offers a statistically validated, generalizable, and interpretable computational framework, representing a rigorous foundation toward automated oral cancer screening support.

PMID:
42855635
Bibliographic data and abstract were imported from PubMed on 10 Oct 2026.

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