TY - GEN
T1 - The Application of Fuzzy Reasoning and Biclustering in Ultrasound Breast Tumor Classification
AU - Zhang, Fan
AU - Huang, Qinghua
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/11
Y1 - 2019/1/11
N2 - The breast tumor is a common disease for females. In the paper, we designed a computer-aided diagnosis (CAD) method for breast tumor which employed the biclustering algorithm and fuzzy inference. The specialist of Sun Yat-sen University helped us collect 500 breast tumor cases. The BI-RADS feature scoring standard was utilized to extract the useful information from ultrasound images. Compared with previous method, the diagnosis process of the proposed method is more easy to be understood. Meanwhile, the performance of the method in this paper is not restricted by the type of image source. The experiment shows that the performance of our method exceeded the previous method which also employed the BI-RADS information in accuracy, positive predictive value (PPV), negative predictive value (NVP), and sensitivity. According to the experiment, the average accuracy achieves 88. S7%, PPV is 86.45%, NVP is 94.24%, specificity reaches 76.55% and sensitivity is 96.85%. The result means that the method in this paper can distinguish the malignant breast tumor precisely.
AB - The breast tumor is a common disease for females. In the paper, we designed a computer-aided diagnosis (CAD) method for breast tumor which employed the biclustering algorithm and fuzzy inference. The specialist of Sun Yat-sen University helped us collect 500 breast tumor cases. The BI-RADS feature scoring standard was utilized to extract the useful information from ultrasound images. Compared with previous method, the diagnosis process of the proposed method is more easy to be understood. Meanwhile, the performance of the method in this paper is not restricted by the type of image source. The experiment shows that the performance of our method exceeded the previous method which also employed the BI-RADS information in accuracy, positive predictive value (PPV), negative predictive value (NVP), and sensitivity. According to the experiment, the average accuracy achieves 88. S7%, PPV is 86.45%, NVP is 94.24%, specificity reaches 76.55% and sensitivity is 96.85%. The result means that the method in this paper can distinguish the malignant breast tumor precisely.
KW - Biclustering
KW - BIRADS
KW - breast tumor
KW - fuzzy
UR - http://www.scopus.com/inward/record.url?scp=85061497091&partnerID=8YFLogxK
U2 - 10.1109/ICARM.2018.8610696
DO - 10.1109/ICARM.2018.8610696
M3 - 会议稿件
AN - SCOPUS:85061497091
T3 - ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
SP - 703
EP - 707
BT - ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2018
Y2 - 18 July 2018 through 20 July 2018
ER -