Deep learning-based recognition of stained tongue coating images

Objective

To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.

Methods

A total of 1 001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1 007 images of pathological (non-stained) tongue coating from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine with lung cancer, diabetes, and hypertension were collected. The tongue images were randomized into the training, validation, and testing datasets in a 7 : 2 : 1 ratio. A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets. The training period was 90 epochs. The model’s performance was evaluated by its accuracy, loss curve, recall, F1 score, confusion matrix, receiver operating characteristic (ROC) curve, and precision-recall (PR) curve in the tasks of predicting stained tongue coating images in the testing dataset. The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine (TCM).

Results

The training results showed that after 90 epochs, the model presented an excellent classification performance. The loss curve and accuracy were stable, showing no signs of overfitting. The model achieved an accuracy, recall, and F1 score of 92%, 91%, and 92%, respectively. The confusion matrix revealed an accuracy of 92% for the model and 69% for TCM practitioners. The areas under the ROC and PR curves were 0.97 and 0.95, respectively. Conclusion: The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners. This model has the potential to assist doctors in identifying false tongue coating and preventing misdiagnosis.