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However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of \u201chandcrafted\u201d texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k\u2013nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of \u201chandcrafted\u201d texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.<\/jats:p>","DOI":"10.1177\/0161734618787447","type":"journal-article","created":{"date-parts":[[2018,7,17]],"date-time":"2018-07-17T10:18:15Z","timestamp":1531822695000},"page":"357-379","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":58,"title":["Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model"],"prefix":"10.1177","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5258-4402","authenticated-orcid":false,"given":"Puja","family":"Bharti","sequence":"first","affiliation":[{"name":"Thapar Institute of Engineering &amp; Technology, Patiala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepti","family":"Mittal","sequence":"additional","affiliation":[{"name":"Thapar Institute of Engineering &amp; Technology, Patiala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rupa","family":"Ananthasivan","sequence":"additional","affiliation":[{"name":"Manipal Hospital, Bangalore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2018,7,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.c1240"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/hep.21347"},{"key":"e_1_3_3_4_2","unstructured":"World Health Organization. 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