Comparison of deep convolutional neural network models with OCT images for dental caries classification

Deep learning techniques specifically deep convolutional neural network (CNN) models, the latest core model of artificial neural networks, provide computer-vision capabilities, including medical and biomedical image analysis and classification. In this paper, we imaged ex vivo human tooth specimens using OCT imaging systems to classify and clarify the accuracy of different tooth samples with and without carious lesions via deep CNN models with transfer-learning and fine-tuning strategies. Collecting a large amount of OCT image data from dental samples can be difficult, and not providing sufficient data for CNN models can lead to overfitting. For these reasons, transfer learning and fine-tuning techniques were utilized in this study. OCT images of human extracted premolar and molar teeth were categorized into three classes. Five deep CNN models, specifically, a basic CNN with three convolutional and max pooling layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19 models were developed and evaluated for OCT image classification of dental caries. In transfer learning, an existing learned model was employed as a feature extractor without changing the weight data, while in fine tuning, an existing learned model was utilized as a feature extractor by relearning some of the weight data. These methods are powerful methods for training deep CNN models without overfitting. This study highlights the performance of various deep learning models for OCT image classification of carious lesions.