Integration of Clinical and Imaging Data for Enhancing Oral Cancer Detection Using Deep Learning

This study aims at unlocking the integration of clinical and imaging data using the deep learning model for enhancing the diagnosis of oral cancer cases. Therefore, for image analysis, in this paper proposed to major on CNNs while for clinical data, our Research used RNNs and hence came up with a unique architecture. The proposed model was trained on the patients’ records and Histopathological Images Database from 5000 patients. As for the overall measure of accuracy of all of the constructed classifiers applying our integrated model the result was 94 %. Specificity achieved for the system was 7% while sensitivity was 93% for the oral cancer. 2%, sensitivity of 69.1%. This is an improvement of the previous methods that required either imaging or clinical data to obtain accuracy approximated at 85-90 percent. As a unique effectiveness factor of increasing the accuracy of oral cancer diagnosis, it is proposed to use an integration of clinical and imaging data through deep learning, which can also result in inclusion of the diagnosis at the early stages of the disease, thereby enhancing the prognosis of the illness.