Research Journal of Pharmacy and Technology

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Classification of Radiolucency in Dental X-Ray Image


Affiliations
1 Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India
     

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Health is the greatest gift to any human being. Growth of the nation depends on the health of every individual. To maintain health and hygiene, a human being must eat good food. For eating healthy food teeth plays an important role. The teeth being a small part of the body plays a very critical part in digestion. Many times due to time constraints patient cannot go to the hospital at right time or wants to get a second opinion. So, in this aspect, the proposed system is created to diagnose the status of the tooth automatically. The anticipated system takes x-ray images as input and classifies the output as a category of radiolucency that it falls under. The classification of the tooth is done by using Multi-Layer Perceptron (MLP), SMO, KNN. Features are extracted using both, Spatial and Frequency domain. Classification is done using Weka tool.

Keywords

Multi-Layer Perceptron (MLP), Random Forest, Sequential Minimal Optimization (SMO), Gray Level Co-occurrence Matrix (GLCM), Fast Fourier Transform (FFT).
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  • Classification of Radiolucency in Dental X-Ray Image

Abstract Views: 253  |  PDF Views: 0

Authors

Carl Jordan Britto
Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India
H. B. Anita
Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India

Abstract


Health is the greatest gift to any human being. Growth of the nation depends on the health of every individual. To maintain health and hygiene, a human being must eat good food. For eating healthy food teeth plays an important role. The teeth being a small part of the body plays a very critical part in digestion. Many times due to time constraints patient cannot go to the hospital at right time or wants to get a second opinion. So, in this aspect, the proposed system is created to diagnose the status of the tooth automatically. The anticipated system takes x-ray images as input and classifies the output as a category of radiolucency that it falls under. The classification of the tooth is done by using Multi-Layer Perceptron (MLP), SMO, KNN. Features are extracted using both, Spatial and Frequency domain. Classification is done using Weka tool.

Keywords


Multi-Layer Perceptron (MLP), Random Forest, Sequential Minimal Optimization (SMO), Gray Level Co-occurrence Matrix (GLCM), Fast Fourier Transform (FFT).

References