Transforming Oral Cancer Care with Convolutional Neural Networks

This research focuses on the application of convolutional neural networks (CNNs) to develop a system for detecting oral cancer from tissue images. The system is designed to enhance the speed and accuracy of detection. While traditional diagnostic methods are effective, they are often timeconsuming and require expert interpretation, which can delay the early detection of critical health conditions. Early detection of oral cancer remains a significant global health challenge. The CNN model was trained on a dataset containing both cancerous and non-cancerous tissue images. The model demonstrated strong learning performance with a training loss of 0.5042 and a training accuracy of $\mathbf{9 4. 4 1 \%}$. The validation process yielded similarly promising results, with a validation loss of 0.5030 and a validation accuracy of $\mathbf{9 4. 7 4 \%}$. Upon testing with unseen data, the model achieved a loss of 0.6893 and a test accuracy of $\mathbf{9 2. 1 7 \%}$. These results confirm that CNNs are highly effective in identifying oral cancer from tissue images, enhancing both the accuracy and efficiency of the detection process. The model’s high performance in training, validation, and testing indicates its potential to reliably identify cancerous tissue, which would assist pathologists in making rapid and accurate diagnoses. The improved diagnostic speed and accuracy have the potential to significantly benefit patients by enabling faster identification and treatment of oral cancer.