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Symmetry 2019, 11(1), 33; https://doi.org/10.3390/sym11010033

Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier

1
Department of Information Technology, Francis Xavier Engineering College, Tirunelveli 627003, India
2
Department of Computer Science & Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur 628215, India
*
Author to whom correspondence should be addressed.
Received: 16 November 2018 / Revised: 22 December 2018 / Accepted: 25 December 2018 / Published: 2 January 2019
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Abstract

Chronic liver disease (CLD), which indicates the inflammatory condition of the liver, leads to cirrhosis or even partial or total liver dysfunction when left untreated. A non-invasive approach for evaluating CLD with computed tomography (CT) images is proposed using an ensemble of classifiers. To accurately classify CLD, the hybrid whale optimization algorithm with simulated annealing (WOA-SA) is used in selecting an optimal set of features. The proposed method employs seven sets of features with a total of 73–3D (three-dimensional) texture features. A hybrid ensemble classifier with support vector machine (SVM), k—Nearest Neighbor (k-NN), and random forest (RF) classifiers are used to classify liver diseases. Experimental analysis is performed on clinical CT images datasets, which include normal liver, fatty liver, metastasis, cirrhosis, and cancerous samples. The optimal features selected using the WOA-SA improve the accuracy of CLD classification for the five classes of diseases mentioned above. The accuracy of the liver classification using ensemble classifier yields approximately 98% with a 95% confidence interval (CI) of (0.7789, 1.0000) and an error rate of 1.9%. The performance of the proposed method is compared with two existing algorithms and the sensitivity and specificity yield an overall average of 96% and 93%, with 95% confidence interval of (0.7513, 1.0000) and (0.7126, 1.0000), respectively. Classification of CLD based on ensemble classifier illustrates the effectiveness of the proposed method and the comparison analysis demonstrates the superiority of the methodology. View Full-Text
Keywords: liver disease; 3D computed tomography liver images; feature extraction; whale optimization algorithm; ensemble of classifiers liver disease; 3D computed tomography liver images; feature extraction; whale optimization algorithm; ensemble of classifiers
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Rajathi, G.I.; Jiji, G.W. Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier. Symmetry 2019, 11, 33.

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