Dental Caries Classification System Using Deep Learning Based Convolutional Neural Network

Abstract

In the modern times, Dental caries is one of the most prevalent diseases of the teeth in the whole world. Almost 90% of the people get affected by cavity. Dental caries is the cavity which occurs due to the remnant food and bacteria. Dental Caries are curable and preventable diseases when it is identified at earlier stage. Dentist uses the radiographic examination in addition with visual tactile inspection to identify the caries. Dentist finds difficult to identify the occlusal, pit and fissure caries. It may lead to sever problem if the cavity left untreated and not identified at the earliest stage. Machine learning can be applied to solve this issue by applying the labelled dataset given by the experienced dentist. In this paper, convolutional based deep learning method is applied to identify the cavity presence in the image. 480 Bite viewing radiography images are collected from the Elsevier standard database. All the input images are resized to 128–128 matrixes. In preprocessing, selective median filter is used to reduce the noise in the image. Pre-processed inputs are given to deep learning model where convolutional neural network with Google Net inception v3 architecture algorithm is implemented. ReLu activation function is used with Google Net to identify the caries that provide the dentists with the precise and optimized results about caries and the area affected. Proposed technique achieves 86.7% accuracy on the testing dataset.