Classification of Fatty and Cirrhosis Liver Using Wavelet-Based Statistical Texture Features andNeural Network Classifier |
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K. Mala,V. Sadasivam. Classification of Fatty and Cirrhosis Liver Using Wavelet-Based Statistical Texture Features andNeural Network Classifier. International Journal of Software and Informatics, 2010,4(2):151~163 |
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Abstract:Computational methods are useful for medical diagnosis because they provide
additional information that cannot be obtained by simple visual interpretation. As a result
an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. The study and development of Probabilistic Neural Network
(PNN), Linear Vector Quantization (LVQ) Neural Network and Back Propagation Neural
Network (BPN) for classification of fatty and cirrhosis liver from Computerized Tomography
(CT) abdominal images is reported in this work. Neural networks are supported by more
conventional image processing operations in order to achieve the objective set. To evaluate
the classifiers, Receiver Operating Characteristic (ROC) analysis is done and the results are
also evaluated by the radiologists. Experimental results show that PNN is a good classifier, giving an accuracy of 95% by holdout method and giving an accuracy of 96% by 10 fold
cross validation method for classifying fatty and cirrhosis liver using wavelet based statistical
texture features. |
keywords:probabilistic neural network linear vector quatization neural network backpropagation neural network biorthogonal wavelet transform simple genetic algorithm medical diagnosis |
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