Abstract

The rising frequency of oral cancer and its devastating impact on public health necessitate the development of more accurate methods for early detection and diagnosis. This study presents a groundbreaking approach that utilizes convolutional neural networks (CNN) to improve the precision of medical image-based oral cancer diagnosis. Our meticulously crafted model, fine-tuned and optimized for oral cancer characteristics through transfer learning with pre-trained models, achieves an impressive accuracy rate of 81%. This study underscores the potential of deep learning methods, particularly CNNs, as highly effective tools for achieving precise and early detection of oral cancer. The results unequivocally demonstrate the efficacy of our model in accurately distinguishing between different oral cancer conditions, thereby paving the way for improved diagnostic accuracy, and contributing to better patient outcomes. Overall, our research significantly advances the evolving landscape of medical image analysis and reinforces the indispensable role of advanced technologies in addressing critical healthcare challenges.