Oral Cancer Detections and Classification Using Region Based Convolutional Neural Network

Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep Learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection.