3066-280XISSN (Online)
Volume 5 , Issue 2 , PP: 34-52, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Khaled Sh. Gaber 1 * , Shahid Mahmood 2
Doi: https://doi.org/10.54216/MOR.050203
Oral cancer remains a significant global health concern, particularly due to the high rates of late-stage detection and the limitations of traditional diagnostic modalities. This study proposes a hybrid diagnostic framework that integrates deep learning with metaheuristic optimization to enhance the accuracy, efficiency, and interpretability of oral cancer classification. The architecture combines convolutional and recurrent neural network components with an adaptive optimization layer designed using swarm intelligence-inspired algorithms. This hybridization enables precise feature selection, architecture tuning, and parameter optimization, resulting in improved generalization and robustness across heterogeneous clinical datasets. The model is further augmented with explainable decision support features, enabling clinicians to visualize lesion relevance and interpret classification outcomes. Empirical evaluations demonstrate superior performance in terms of sensitivity, specificity, and computational efficiency compared to conventional training strategies. Additionally, the proposed framework is designed for portability and scalability, supporting potential deployment in mobile and edge-based diagnostic systems. The integration of interpretability, fairness constraints, and clinical adaptability underscores the model’s readiness for real-world implementation. This work contributes to the growing field of intelligent medical diagnostics and highlights the transformative potential of metaheuristic optimization in addressing complex, high-dimensional clinical classification tasks.
Metaheuristic optimization , Deep learning , Feature selection , Oral cancer , Intelligent diagnostics
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