Document Type : Research Articles
Authors
1
Department of Electrical and Electronics Engineering, Saveetha Engineering College, Saveetha Nagar, Thandalam, Chennai : 602105, Tamilnadu, India.
2
Department of Electronics and Communciation Engineering, Easwari Engineering College, Rama-puram, Chennai, Tamilnadu, India.
Abstract
Objective: Early 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. Methods: A 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). Result: The 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. Conclusion: The 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.
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