Abstract
Background: Oral squamous cell carcinoma (OSCC) has high mortality, especially in underdeveloped regions. Traditional Broders’ grading is subjective and lacks prognostic accuracy, ignoring key tumor features like invasion patterns and tumor-stroma interactions. While the WHO 4th and AJCC 8th editions introduced updates, their clinical utility remains limited due to inter-observer variability and the exclusion of molecular markers. Objective: To enhance OSCC risk stratification by developing a Bayesian-optimized MobileXNet (BO-MobileXNet) model using Hematoxylin and Eosin (H&E) Whole Slide Imaging (WSI). This model improves classification efficiency and accuracy over conventional CNNs, with pathologists’ validation ensuring clinical relevance. Methods: Digitized WSIs from OSCC archival FFPE blocks were preprocessed through normalization and augmentation. A Deep Learning (DL) model was trained and optimized using Bayesian hyperparameter tuning for OSCC risk classification. Results: The BO-MobileXNet model achieved an accuracy of 89 % with a 95 % Confidence Interval (CI) of 76.03 %–100 %, demonstrating high classification performance. The AUC-ROC score of 0.995 (CI: 0.992–0.998) indicates strong sensitivity and specificity. The binomial test yielded a p-value of 0.0032 (p < 0.05), confirming a statistically significant improvement over traditional CNN models. Additionally, a user-friendly interface was developed to facilitate seamless clinical integration. Conclusion: This AI-powered approach improves OSCC risk classification, accelerates diagnosis and aids clinical decision-making. The process of diagnosing has been automated which increases the efficiency and is cost effective. Despite its great accuracy, validation with larger and more diversified datasets is required.