Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network
An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radi...
Saved in:
Published in | Computational intelligence and neuroscience Vol. 2022; pp. 3500552 - 7 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Hindawi
30.04.2022
John Wiley & Sons, Inc Hindawi Limited |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Academic Editor: Gopal Chaudhary |
ISSN: | 1687-5265 1687-5273 |
DOI: | 10.1155/2022/3500552 |