  Oral cancer has more than 83% survival rate if detected in its early stages,
however, only 29% of cases are currently detected early. Deep learning
techniques can detect patterns of oral cancer cells and can aid in its early
detection. In this work, we present the first results of neural networks for
oral cancer detection using microscopic images. We compare numerous
state-of-the-art models via transfer learning approach and collect and release
an augmented dataset of high-quality microscopic images of oral cancer. We
present a comprehensive study of different models and report their performance
on this type of data. Overall, we obtain a 10-15% absolute improvement with
transfer learning methods compared to a simple Convolutional Neural Network
baseline. Ablation studies show the added benefit of data augmentation
techniques with finetuning for this task.
