Early Detection of Oral Cancer Using Image Processing and Computational Techniques

This study aimed to develop and evaluate a platform for the detection of oral cancer using biopsy samples of histopathological images. This study demonstrated the effectiveness of image pre-processing techniques in improving the performance of the Convolutional Neural Network (CNN) models. These findings suggest that the use of advanced computational techniques in conjunction with histopathological images can significantly improve the detection of oral cancer in clinical practice, potentially leading to better patient outcomes and a reduction in healthcare costs. Using CNN, we developed models VGG16, VGG19, InceptionV3, AlexNet, ResNet50 that showed high sensitivity and specificity for detecting oral cancer, with the testing accuracy of 84%, 82%, 67%, 76%, 42% respectively. The study underscores the potential of deep learning and image-processing methods to revolutionize cancer detection and diagnosis.