ICTACT Journal on Soft Computing

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Artificial Neural Networks to Detect Facial Abnormalities through Cephalometric Radiography using Bjork Analysis


Affiliations
1 Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, India
     

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The dental and skeletal relationships in the head are studied in Cephalometric analysis. This research work addresses Bjork’s analysis for the classification of patients. In this research work, the backpropagation neural network (BPNN), and generalized regression neural network (GRNN) classifiers are used and studied for the diagnosis of Cephalometric analysis. In this study, a total of 304 (male 109, female 195) patient’s case records were collected for this study. All the collected clinical data are used for classification. For training and testing the proposed models, patients' data were separated by four-fold cross-validation. Based on Bjork analysis, experimental results show that GRNN provided achieving the performance of 97.39% of good classification results when compared to the BPNN model. The GRNN approach is feasible and was found to be achieving a performance of 97.39% of the correct detection of patients.

Keywords

Bjork’s Analysis, Cephalometric Analysis, Back Propagation Neural Network, Generalized Regression Neural Network.
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  • Artificial Neural Networks to Detect Facial Abnormalities through Cephalometric Radiography using Bjork Analysis

Abstract Views: 247  |  PDF Views: 2

Authors

S. Anbazhagan
Department of Electrical Engineering, Faculty of Engineering and Technology, Annamalai University, India

Abstract


The dental and skeletal relationships in the head are studied in Cephalometric analysis. This research work addresses Bjork’s analysis for the classification of patients. In this research work, the backpropagation neural network (BPNN), and generalized regression neural network (GRNN) classifiers are used and studied for the diagnosis of Cephalometric analysis. In this study, a total of 304 (male 109, female 195) patient’s case records were collected for this study. All the collected clinical data are used for classification. For training and testing the proposed models, patients' data were separated by four-fold cross-validation. Based on Bjork analysis, experimental results show that GRNN provided achieving the performance of 97.39% of good classification results when compared to the BPNN model. The GRNN approach is feasible and was found to be achieving a performance of 97.39% of the correct detection of patients.

Keywords


Bjork’s Analysis, Cephalometric Analysis, Back Propagation Neural Network, Generalized Regression Neural Network.

References