  Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral
epithelium. Despite their high impact on mortality, sufficient screening
methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are
mostly diagnosed at a late stage. Early detection and accurate outline
estimation of OSCCs would lead to a better curative outcome and an reduction in
recurrence rates after surgical treatment.
  Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical
images for in vivo cell structure analysis. Recent CLE studies showed great
prospects for a reliable, real-time ultrastructural imaging of OSCC in situ.
  We present and evaluate a novel automatic approach for a highly accurate OSCC
diagnosis using deep learning technologies on CLE images. The method is
compared against textural feature-based machine learning approaches that
represent the current state of the art.
  For this work, CLE image sequences (7894 images) from patients diagnosed with
OSCC were obtained from 4 specific locations in the oral cavity, including the
OSCC lesion. The present approach is found to outperform the state of the art
in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean
accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
