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Abstract
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Article Information:
Genetic Algorithm Optimized Back Propagation Neural Network for Knee Osteoarthritis Classification
Jian WeiKoh, Tian-Swee Tan, Zhi EnChuah, Sarah Samson Soh, Muhammad Arif and KahMeng Leong
Corresponding Author: Tian-swee Tan
Submitted: March 22, 2014
Accepted: July 01, 2014
Published: October 25, 2014 |
Abstract:
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Osteoarthritis (OA) is the most common form of arthritis that caused by degeneration of articular cartilage, which function as shock absorption cushion in our joint. The most common joints that infected by osteoarthritis are hand, hip, spine and knee. Knee osteoarthritis is the focus in this study. These days, Magnetic Resonance Imaging (MRI) technique is widely applied in diagnosis the progression of osteoarthritis due to the ability to display the contrast between bone and cartilage. Traditionally, interpretation of MR image is done manually by physicians who are very inconsistent and time consuming. Hence, automated classifier is needed for minimize the processing time of classification. In this study, genetic algorithm optimized neural network technique is used for the knee osteoarthritis classification. This classifier consists of 4 stages, which are feature extraction by Discrete Wavelet Transform (DWT), training stage of neural network, testing stage of neural network and optimization stage by Genetic Algorithm (GA). This technique obtained 98.5% of classification accuracy when training and 94.67% on testing stage. Besides, classification time is reduced by 17.24% after optimization of the neural network.
Key words: Classification, discrete wavelet transform, genetic algorithm, knee osteoarthritis, neural network, ,
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Cite this Reference:
Jian WeiKoh, Tian-Swee Tan, Zhi EnChuah, Sarah Samson Soh, Muhammad Arif and KahMeng Leong, . Genetic Algorithm Optimized Back Propagation Neural Network for Knee Osteoarthritis Classification. Research Journal of Applied Sciences, Engineering and Technology, (16): 1787-1793.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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