Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model

Ultrasonic Imaging
Puja BhartiRupa Ananthasivan

Abstract

Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality. 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 "handcrafted" 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-nearest neighbor (k-NN), support vector machine (SVM), and rotatio...Continue Reading

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Citations

Sep 4, 2018·Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine·Puja BhartiRupa Ananthasivan
Oct 23, 2020·World Journal of Gastroenterology : WJG·Miguel Jiménez Pérez, Rocío González Grande
Sep 2, 2021·Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine·Boran ZhouTian Liu

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Methods Mentioned

BETA
Feature Extraction
PCA

Software Mentioned

VGGNet
GLDM
Matlab
ResNet
AlexNet
GoogleNet

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