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Stacked autoencoder for classification of glioma grade III and grade IV

Show simple item record Patil, S. Naik, G.M. Pai, R. Gad, R.S. 2018-08-03T04:32:47Z 2018-08-03T04:32:47Z 2018
dc.identifier.citation Biomedical Signal Processing and Control. 46; 2018; 67-75. en_US
dc.description.abstract Invention of the microarray technology has rendered it possible to inspect the whole genome at once in cancer classification. However, in order to curtail the computational complexity and augment the accuracy of cancer classification, it is essential to sift the vast microarray data for the informative genes. In this paper, Thresholding and Ratio methods are presented, individually as well as conjointly (hybrid method) to choose optimal gene subset from the microarray data. Moreover, Discrete Wavelet Transform (DWT) is deployed to pare the size of microarray data still further. The classification is accomplished by using various neural network algorithms and Stacked Autoencoder algorithm. The results of classification are compared for number of thresholds, ratios, wavelets and classification algorithms. It is observed that the Stacked Autoencoder network trained by Back Propagation algorithm delivers the best results in terms of classification accuracy and number of genes. en_US
dc.publisher Elsevier en_US
dc.subject Electronics en_US
dc.title Stacked autoencoder for classification of glioma grade III and grade IV en_US
dc.type Journal article en_US
dc.identifier.impf y

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