aceA Statistically Validated and Explainable Deep Learning Framework for Oral Cancer Classification
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-tu…
A hybrid pipeline pairing a pre-trained ResNet50 backbone with an SVM-RBF classifier outperformed end-to-end CNNs on oral cancer classification and kept strong accuracy on an independent external dataset.
The authors frame the pipeline only as a foundation toward automated oral cancer screening support, and accuracy was lower on the external dataset than in cross-validation.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-09
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Truvace Impact Record TRV-2026-1638, v1: “A Statistically Validated and Explainable Deep Learning Framework for Oral Cancer Classification.” Truvace, 2026-10-17. /record/TRV-2026-1638 (accessed at citation time). sha256 b7b3cdfba7e36ba0…
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