A novel approach for liver image classification: PH-C-ELM

Abstract Classification of liver masses is one of the hot topics in the literature. This paper proposes a hybrid method of using Convolutional Neural Network (CNN) and Discrete Wavelet Transform- Singular Value Decomposition (DWT-SVD) based perceptual hash function. The aim of the proposed method is to reduce the execution time of CNN architecture, space of liver images occupied on the hard disk and maintain the classification performance above an acceptable threshold. The proposed method has been designed for classifying malignant and benign masses from liver CT images. The most important features required for classification are achieved by the acquisition of salient features using Perceptual hash functions. Experimental evaluation was performed with 5-fold cross validation on a set of 200 CT images, 100 of benign tumors and 100 of malignant tumors. Results showed that the CNN features achieved high classification performance with different classifiers. However, experimental results show that CNN features achieved better classification performance with ELM, where ELM simulation results validated output data with success 97.3%.