Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancer

Oral cancer is most prevalent in low- and middle-income countries where it is associated with late diagnosis. A significant factor for this is the limited access to specialist diagnosis. The use of artificial intelligence for decision making on oral cavity images has the potential to improve cancer management and survival rates. This study forms part of the MeMoSA® (Mobile Mouth Screening Anywhere) project. In this paper, we extended on our previous deep learning work and focused on the binary image classification of ‘referral’ vs. ‘non-referral’. Transfer learning was applied, with several common pre-trained deep convolutional neural network architectures compared for the task of fine-tuning to a small oral image dataset. Improvements to our previous work were made, with an accuracy of 80.88% achieved and a corresponding sensitivity of 85.71% and specificity of 76.42%.