Deep Learning Techniques for the Detection and Classification of Oral Cancer Using Histopathological Images

Early oral cancer identification is crucial, which affects millions of people worldwide, is crucial for enhancing patient prognosis and survival rates. The primary method for identifying and categorizing oral cancer is histological examination, however, it is a time-consuming, labour-intensive process that depends on pathologists' knowledge. Using histopathology pictures, deep learning approaches have shown tremendous potential for automating the detection and categorization of oral cancer. In this research, DenseNet-based deep learning model architecture is suggested for the precise identification and categorization of oral cancer in histopathology pictures, achieving a remarkable accuracy of 95%. Before training the CNN-based model on a substantial dataset of enhanced images, it is beneficial to preprocess the photos to extract relevant features, enhancing the training dataset's variety and quality. After the final model has been trained, a web application will use it. that doctors can use to quickly and accurately identify oral cancer. The accuracy and efficacy of oral cancer detection could be greatly increased thanks to our suggested methodology, which would also enhance patient outcomes and survival rates. The findings of this work show that it is possible to automatically detect and categorize oral cancer in histopathological pictures using deep learning algorithms, and further investigation in this field can help to develop more precise and effective diagnostic tools.