Oral Cancer Histopathological Detection and Diagnosis Using Hybrid AI Deep Learning Methods

This study introduces a novel deep learning-based approach for the early detection and classification of oral cancer using medical images. The rapid progression of oral cancer to critical regions such as the lips, gums, and inner cheeks underscores the importance of early diagnosis, as delayed detection can lead to severe consequences, including mortality. To address this challenge, we leveraged computer-aided diagnostic (CAD) technologies, integrating convolutional neural networks (CNNs) with an advanced Deep Convolutional Neural Network (DCNN) optimized through an evolutionary algorithm to enhance diagnostic accuracy. Our proposed approach underwent rigorous evaluation against established models, including Digital Network, Bayesian, and Artificial Neural Network (ANN) frameworks. Using the ‘Oral Disease (Lips and Tongue) Images' dataset, the method achieved an impressive accuracy of 97.71%, a sensitivity of 92.37%, and an F1-score of 94.65%. These results highlight the method's capability for reliable and precise classification of oral cancer, offering a promising tool for early diagnosis and improved clinical outcomes.