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2018 | OriginalPaper | Chapter

Computerized Features for LI-RADS Based Computer-Aided Diagnosis of Liver Lesions

Authors : Mingzhong Chen, Lanfen Lin, Qingqing Chen, Hongjie Hu, Qiaowei Zhang, Yingying Xu, Yen-Wei Chen

Published in: Innovation in Medicine and Healthcare 2017

Publisher: Springer International Publishing

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Abstract

Liver Imaging Reporting Data System (LI-RADS) aims to standardize liver lesion imaging findings and diagnostic reports, and it is used as an accurate noninvasive diagnosis and staging method of hepatocellular carcinoma (HCC) nowadays. In this study, we proposed several computerized features for LI-RADS based computer-aided diagnosis of liver lesions. We used several popular machining learning approaches for computerized LI-RADS classification (benign and malignant classification) with our proposed features. The performance of each method was evaluated by using ROC curve and the best AUC score was 0.965 reached by the gradient boosting classifier.

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previous chapter Detection of Liver Tumor Candidates from CT Images Using Deep Convolutional Neural Networks
next chapter Improving Measurement Accuracy for Rheumatoid Arthritis Medical Examinations
Literature
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Metadata
Title
Computerized Features for LI-RADS Based Computer-Aided Diagnosis of Liver Lesions
Authors
Mingzhong Chen
Lanfen Lin
Qingqing Chen
Hongjie Hu
Qiaowei Zhang
Yingying Xu
Yen-Wei Chen
Copyright Year
2018
Book
Innovation in Medicine and Healthcare 2017

Print ISBN: 978-3-319-59396-8

Electronic ISBN: 978-3-319-59397-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-59397-5_16

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