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A new breast tumor ultrasonography CAD system based on decision tree and BI-RADS features

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Abstract

In this paper, we present a novel computer-aided diagnostic (CAD) system based on the Breast Imaging Reporting and Data System (BI-RADS) terminology scores of screening ultrasonography (US). The decision tree algorithm is adopted to analyze the BI-RADS information to differentiate between the malignant and benign breast tumors. Although many ultrasonography CAD systems have been developed for decades, there are still some problems in clinical practice. Previous CAD systems are opaque for clinicians and cannot process the ultrasound image from different ultrasound machines. This study proposes a novel CAD system utilizing BI-RADS scoring standard and Classification and Regression Tree (CART) algorithm to overcome the two problems. The original dataset consists of 1300 ultrasound breast images. Three well-experienced clinicians evaluated all of the images according to the BI-RADS feature scoring standard. Subsequently, each image could be transformed into a 25 × 1 vector. The CART algorithm was finally used to classify these vectors. In the experiments, we used the oversampling method to balance the number of malignant samples and benign samples. The 5-fold cross validation was employed to evaluate the performance of the system. The accuracy reached 94.58%, the specificity was 98.84%, the sensitivity was 90.80%, the positive predictive value (PPV) was 98.91% and the negative predictive value (NVP) was 90.56%. The experiment results show that the proposed system can obtain a sufficient performance in the breast diagnosis and can effectively recognize the benign breast tumors in BI-RADS 3.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Nos. 61372007 and 61571193), Guangzhou Key Lab of Body Data Science (No.201605030011), Natural Science Foundation of Guangdong Province, China (Nos. 2017A030312006 and 2015A030313210), Fundamental Research Funds for the Central Universities (NO.2015ZM138), Project of Science and Technology Department of Guangdong province (2014A050503020, 2016A010101021, 2016A010101022 and 2016A010101023), Science and Technology Program of Guangzhou (no. 201704020134).

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Authors and Affiliations

  1. School of Mechanical Engineering, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, People’s Republic of China

    Qinghua Huang

  2. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Qinghua Huang

  3. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China

    Qinghua Huang & Fan Zhang

  4. Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi, 710119, China

    Xuelong Li

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  1. Qinghua Huang
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  2. Fan Zhang
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Correspondence to Qinghua Huang.

Additional information

This article belongs to the Topical Collection: Special issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

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Huang, Q., Zhang, F. & Li, X. A new breast tumor ultrasonography CAD system based on decision tree and BI-RADS features. World Wide Web 21, 1491–1504 (2018). https://doi.org/10.1007/s11280-017-0522-5

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  • DOI: https://doi.org/10.1007/s11280-017-0522-5

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