Dental Image Segmentation and Classification Using Inception Resnetv2

Abstract

The automated process for dental caries detection draws increasing attention with the technological innovation in machine learning methods. This is a core issue in dental diseases especially in the detection of caries as it leads to serious health ailments. This paper takes an effort to adequately segment and identify dental diseases. There are four main steps. The preprocessing technique uses binary histogram equalization which increases the texture region visibility for the caries detected on dental images. The novel technique of segmentation with Curvilinear Semantic Deep Convolutional Neural Network (CSDCNN) is proposed in this paper . The segmentation is followed by the proposed Inception resnetV2, which acts as the classification technique to determine the caries in dental images. The proposed segmentation algorithm is used to determine a dental degree of membership. The inception is brought out with different scales of information, which relates to various input images as data. An examination of the x-ray images will detect the impact of illness on a tooth. Particularly for the segmentation and classification mission, we deemed four diseases: dental caries, periapical infection, periodontal, and pericoronal diseases. Based on the number of input functional parameters, the Inception resnetV2 classifies different image categories effectively. The proposed Inception resnetV2 has become the most effective tool in machine learning to solve problems like image classification with a high order of accuracy. The average accuracy of the device proposed is 94.51%. This provides higher classification accuracy when compared to other existing methods.