DocumentCode :
692665
Title :
Breast tumor detection in double views mammography based on Simple Bias
Author :
Shiya Zhang ; Zhongzhou Chen ; Sheng Gu ; Xihe Qiu ; Qixun Qu ; Zhiqiong Wang
Author_Institution :
Sino-Dutch Biomed. & Inf. Eng. Sch., Northeastern Univ., Shenyang, China
fYear :
2013
fDate :
19-20 Oct. 2013
Firstpage :
240
Lastpage :
244
Abstract :
Breast tumor detection is a most effective way to immunized against mammary cancer. It is known that the sort algorithm of extreme learning machine(ELM), in view of the feature model for breast X-ray image, is being applied in the computer aided detection of breast masses. On the basis of all these, it is raised in this paper that marking for the suspicious region in the double view mammography by the use of ELM, then classifying the result of double views marking by using the Simple Bias classifier and finally gaining the detection result. The experiment with 444 cases or 222 pair of X-ray mammography from Liao Ning Province Cancer Hospital shows that, the breast tumor detection in double views mammography based on Simple Bias is an available and effective way to detect breast tumor. Key Words: Extreme learning machine, Simple Bias, mammography, double views, tumor detection.
Keywords :
cancer; image classification; learning (artificial intelligence); mammography; medical image processing; tumours; ELM; Liao Ning Province Cancer Hospital; Simple Bias classifier; X-ray mammography; breast X-ray image; breast masses; breast tumor detection; computer aided detection; double view mammography; double view marking; extreme learning machine; feature model; mammary cancer; sort algorithm; suspicious region; Accuracy; Breast tumors; Feature extraction; Testing; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Medical Imaging Physics and Engineering (ICMIPE), 2013 IEEE International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4799-6305-8
Type :
conf
DOI :
10.1109/ICMIPE.2013.6864543
Filename :
6864543
Link To Document :
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