Oral Cancer Diagnosis Using an Optimized InceptionV3 Model Powered by the Aquila Metaheuristic Algorithm.

objectiveEarly and accurate detection of oral cancer plays a pivotal role in improving patient prognosis and survival rates. Deep learning (DL) models have shown promise in automating medical image classification; however, performance optimization remains a challenge due to complex network configurations and hyperparameter dependencies. This study introduces an enhanced diagnostic framework combining the InceptionV3 convolutional neural network with the Aquila Optimizer (AO), a nature-inspired metaheuristic algorithm, to achieve superior classification accuracy in identifying oral cancer lesions.

methodsA standardized dataset of labeled oral lesion images, including both benign and malignant cases captured via mobile and intraoral cameras, was used for training. The InceptionV3 model, initially pre-trained, was fine-tuned for binary classification tasks. AO was employed to optimize the hyperparameters by defining a search space and iteratively improving model performance through accuracy maximization and loss minimization strategies. The optimized model was compared against leading architectures such as AlexNet, MobileNet, Xception, ResNet-50, and the original InceptionV3, using comprehensive performance indicators like accuracy, precision, recall, F1-score, AUC-ROC, specificity, log loss, and Matthews Correlation Coefficient (MCC).

resultThe proposed AO-InceptionV3 model consistently outperformed the other DL architectures across all metrics. It achieved a classification accuracy of 97.80%, precision of 97.81%, recall of 97.79%, and an MCC of 0.956, while maintaining a low log loss of 0.0735 and an AUC-ROC of 99.81%. Visual analyses, including ROC curves and 3D plots, reinforced the robustness and reliability of the model in distinguishing between benign and malignant lesions with minimal inference time.

conclusionThe integration of the Aquila Optimizer into the InceptionV3 architecture significantly improves the diagnostic performance of DL models for oral cancer detection. The proposed framework demonstrates excellent potential for real-time clinical deployment, offering high accuracy, efficiency, and reliability, and sets a benchmark for future AI-driven cancer diagnostic systems.