Deep learning system for the differential diagnosis of oral mucosal lesions through clinical photographic imaging

Oral mucosal lesions are associated with a variety of pathological conditions. Most deep-learning-based convolutional neural network (CNN) systems for computer-aided diagnosis of oral lesions have typically concentrated on determining limited aspects of differential diagnosis. This study aimed to develop a CNN-based diagnostic model capable of classifying clinical photographs of oral ulcerative and associated lesions into five different diagnoses, thereby assisting clinicians in making accurate differential diagnoses.

A set of clinical images were selected, including 506 images of five different diagnoses. The images were pre-processed and randomly divided into two sets for training and testing the CNN model. The model architecture was composed of convolutional layers, batch normalization layers, max pooling layers, the dropout layer and fully-connected layers. Evaluation metrics included weighted-precision, weighted-recall, weighted-F1 score, average specificity, Cohen's Kappa coefficient, normalized confusion matrix and AUC.

The overall performance for the image classification showed a weighted-precision of 88.8%, a weighted-recall of 88.2%, a weighted-F1 score of 0.878, an average pecificity of 97.0%, a Kappa coefficient of 0.851, and an average AUC of 0.985.

The model achieved a decent classification performance (overall AUC=0.985), showing the capacity to discern between benign and malignant potential lesions, and laid the foundation of a novel tool that can help clinical differential diagnosis of oral mucosal lesions. The main challenges were the small and imbalanced dataset. Enlarging the minority classes, incorporating more oral mucosal lesion diagnoses, employing transfer learning and cross-validation might be included in future works to optimize the image classification model.