Oropharyngeal Cancer Prediction and Optimization Using Improved VGG16 with Grey Wolf Optimizer

Oropharyngeal cancer, a subtype of head and neck cancer, presents significant challenges in early detection and treatment. In this paper, we propose a novel approach for predicting and optimizing oropharyngeal cancer using an improved VGG16 architecture with the Grey Wolf Optimizer (GWO) algorithm. The utilization of deep learning techniques has shown promise in medical image analysis, particularly in cancer detection, due to their ability to extract complex features from imaging data. The VGG16 architecture, known for its deep convolutional layers, is enhanced with additional layers and regularization techniques to improve its predictive performance. We utilize a comprehensive dataset comprising high-resolution medical images of oropharyngeal tissues for training and evaluation purposes. The dataset is preprocessed to enhance image quality and standardize features across samples. Subsequently, the proposed model is trained using this dataset, with the objective of accurately classifying images as either cancerous or non-cancerous. Experimental results demonstrate the effectiveness of the proposed approach in predicting oropharyngeal cancer with high accuracy and efficiency. Compared to traditional methods, the improved VGG16 architecture combined with the Grey Wolf Optimizer achieves superior performance in terms of both prediction accuracy and convergence speed. Moreover, the model exhibits robustness against variations in imaging conditions and patient demographics.