Deep learning model for tongue cancer diagnosis using endoscopic images.In this study, we developed a deep learning model to identify patients with tongue cancer based on a validated dataset comprising oral endoscopic images.  To calculate the probability of malignancy using various convolutional neural network (CNN) architectures, several deep learning models were developed.  The deep learning model, general physicians, and oncology specialists had sensitivities of 81.1%, 77.3%, and 91.7%; specificities of 86.8%, 75.0%, and 90.9%; and accuracies of 84.7%, 75.9%, and 91.2%, respectively.  The deep learning model developed based on the verified endoscopic image dataset showed acceptable performance in tongue cancer diagnosis.
Deep learning model
The results indicate that the best model was DenseNet169 (AUROC 0.895 and AUPRC 0.918). 
DenseNet169