Genetic Algorithms Based Approach for Dental Caries Detection using Back Propagation Neural Network
Paras Tripathi1, C. Malathy2, M. Prabhakaran3
1Paras Tripathi, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai (Tamil Nadu), India.
2C. Malathy, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai (Tamil Nadu), India.
3M. Prabhakaran, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai (Tamil Nadu), India.
Manuscript received on 21 May 2019 | Revised Manuscript received on 07 June 2019 | Manuscript Published on 15 June 2019 | PP: 316-319 | Volume-8 Issue-1S2 May 2019 | Retrieval Number: A00730581S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The detection of dental caries from radiograph is a very challenging task for Dentists, often early forming caries are overlooked or misclassified. The goal is to assist dentists to detect these caries in early stages so that the severity of the decay caused by delayed treatment can be avoided. To solve this problem a system is proposed which is capable of recognizing dental caries from bitewing radiography. The dental caries radiograph has a certain number of grey level pixels which are a differentiating factor from normal teeth. Therefore, the system utilizes Local Binary Pattern (LBP) to extract second order statistical texture features. These extracted features would be utilized by a backpropagation neural network to characterize the severity of caries. The Hybrid approach will help further to optimize the hyperparameter problem in the neural network and increase accuracy in prediction.
Keywords: Local Binary Pattern, Backpropagation, Neural Networks, Genetic Algorithm.
Scope of the Article: Neural Information Processing