Detection Dental Cysts and Tumors Using Convolution Neural Network

Abstract

The dental cysts A wide range of types. These vary From totally kind to harmful, and can be difficult in clinical practice to identify the exact type of cyst. Accurate pre-operative identification of these cysts will Helping oral and maxillofacial specialists in planning suitable treatment. This work describes an automated algorithm, which classifies of the cysts by using Dental  X-ray images. Methods: In the proposed system the dental X-ray images undergo different image processing steps. Dataset contains 114 X-ray image these images which classify into nine types of the diseases. These diseases Includes  cysts. In this study split the dataset Into two sections a preparation and a test part. Results: Both accuracy and loss were calculated using CNN depending on the number of attempts to teach the algorithm and the percentage of algorithm education. Conclusions: Cysts diagnosed using CNN based on optical panoramic radiographic photographs with a accuracy equal to that of Oral and Maxillofacial Diagnosis. These outcomes show that CNN may help screen for cysts In considerably less period.