Using convolutional neural network for diabetes mellitus diagnosis based on tongue images

Tongue diagnosis plays a great role in traditional Chinese medicine. Diabetes mellitus (DM) diagnosis is a significant branch of tongue diagnosis. In recent years, many algorithms have been proposed to aid DM diagnosis based on tongue images. However, most of the previous studies are based on the traditional machine learning and extract only low-level features, such as colour and texture. Here, the authors used a convolutional neural network for tongue image classification by extracting and using the high-level features of tongue images. They conducted an experiment on a set of 422 DM images and 422 healthy images, which were captured by the specialised device. In order to solve the problem of a small dataset, the authors used a pre-trained model to fine-tune parameters of the network, which is a kind of transfer learning way to accelerate the training speed and improve the accuracy. Finally, the authors compared their experiment with the other state-of-the-art algorithms of DM diagnosis, and the results show that their method has the best performance in terms of many assessment criteria.