A novel approach for liver image classification: PH-C-ELM. (April 2019)

• Record Type: Journal Article
• Authors: Doğantekin, Akif; Özyurt, Fatih; Avcı, Engin; Koç, Mustafa
• Is Part Of: Measurement. Volume 137(2019)
• Page Start: 332
• Page End: 338
• Publication Date: 2019-04
• Subjects: Convolutional neural network -- Extreme learning machine -- Perceptual hash -- Classification of liver masses; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.01.060
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A block-based perceptual hash function is developed. Size of images covered on the hard disk reduced by between 20 and 180 times. CNN architecture is used as feature extractor to avoid manual feature extraction. These features are used in various classification (SVM-KNN-ELM) algorithms. A new hybrid PH-C-ELM method is proposed for liver image classification. 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%.