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Deep Learning for Pixel-based Edge Models Classification of Tertiary Dentine Images

Prevalence of dental caries remains significant clinical problems. The dental caries disease is treated by doing pulp capping. The treatment is evaluated by visually observing the tertiary dentine on x-ray images. This conventional evaluation is not accurate and subjective among the doctors. In order to assist doctor, this research proposed implementation of deep learning method to classify pixel-based edge images of tertiary dentine. The method involves x-ray image data collection, edge modelling, edge training and testing. Eleven pixel-based edge models were defined which consist of eight edge models and three non-edge models. A total number of 660 edge images were extracted from tertiary dentine images and used Convolutional Neural Network deep learning for training and testing. The overall classification of edge images was validated using cross validation method and performed 91% of classification accuracy.