A deep learning mucosal segmentation algorithm for a mobile detection of oral cancer (mDOC) platform

We developed a novel deep-learning based algorithm for a mobile Detection of Oral Cancer (mDOC) platform that captures white light and auto-fluorescence images of the oral cavity. The algorithm first segments images and subsequently identifies suspicious lesions in need of further review by an expert clinician. Preliminary results show a dice score accuracy between ground truth annotated and the network produced segmentation to be higher than 0.9 for the network architectures we tested. This fully automated pipeline enables a data-driven approach with the potential to aid faster diagnosis in the clinic and earlier detection of oral lesions that can ultimately improve patient outcomes.