Detection of dental diseases from radiographic 2d dental image using hybrid graph-cut technique and convolutional neural network. (November 2019) - Al Kheraif, Abdulaziz A.; Wahba, Ashraf A.; Fouad, H.
Highlights: Now a days the major oral health issues of man is crucial an area of research. This paper traces classification, segmentation and pre-processing of dental images. Histogram based adaptive approach is used in the radiographic X-ray 2d dental Image. This is useful to distinguish the foreground teeth & the regions of background bones. Abstract: In the present scenario the major oral health issues of man is crucial an area of research. The Data mining techniques, image processing, and Computational intelligence techniques are playing a vital role in biomedical research. Dental image processing helps to improve the early detection and classification of the diagnostic process to make accurate decisions. The radiographic 2d dental image is widely utilized for analytic thinking of several dental disorders. In this paper traces the complete steps such as classification and segmentation as well as pre-processing of dental images has been carried out. In the pre-processing, histogram based on adaptive approach is used to stretch the contrast and equalize the brightness throughout the radiographic X-ray 2d dental Image. This operation is useful to distinguish the foreground teeth and the regions of background bones. Separation of dental 2d images into regions corresponding to the objects is a fundamental step of segmentation. The hybrid graph cut segmentation is used to segment the oral cavity and its tissues. In this research deep learning based convolution neural network (CNN) has been used to process the dental image and shows promising outcomes with 97.07% accuracy.