  Discovering oral cavity cancer (OCC) at an early stage is an effective way to
increase patient survival rate. However, current initial screening process is
done manually and is expensive for the average individual, especially in
developing countries worldwide. This problem is further compounded due to the
lack of specialists in such areas. Automating the initial screening process
using artificial intelligence (AI) to detect pre-cancerous lesions can prove to
be an effective and inexpensive technique that would allow patients to be
triaged accordingly to receive appropriate clinical management. In this study,
we have applied and evaluated the efficacy of six deep convolutional neural
network (DCNN) models using transfer learning, for identifying pre-cancerous
tongue lesions directly using a small data set of clinically annotated
photographic images to diagnose early signs of OCC. DCNN model based on Vgg19
architecture was able to differentiate between benign and pre-cancerous tongue
lesions with a mean classification accuracy of 0.98, sensitivity 0.89 and
specificity 0.97. Additionally, the ResNet50 DCNN model was able to distinguish
between five types of tongue lesions i.e. hairy tongue, fissured tongue,
geographic tongue, strawberry tongue and oral hairy leukoplakia with a mean
classification accuracy of 0.97. Preliminary results using an (AI+Physician)
ensemble model demonstrate that an automated initial screening process of
tongue lesions using DCNNs can achieve near-human level classification
performance for diagnosing early signs of OCC in patients.
