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A Computer Aided Diagnosis System in Mammography Using Artificial Neural Networks
May 27-May 30
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.932008 International Conference on BioM ...
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Guodong Zhang, Peiyu Yan, Hong Zhao, Xin Zhang, "A Computer Aided Diagnosis System in Mammography Using Artificial Neural Networks," BioMedical Engineering and Informatics, International Conference on, vol. 2, pp. 823-826, 2008 International Conference on BioMedical Engineering and Informatics, 2008.
 
 
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@article{ 10.1109/BMEI.2008.93,
author = {Guodong Zhang and Peiyu Yan and Hong Zhao and Xin Zhang},
title = {A Computer Aided Diagnosis System in Mammography Using Artificial Neural Networks},
journal ={BioMedical Engineering and Informatics, International Conference on},
volume = {2},
year = {2008},
isbn = {978-0-7695-3118-2},
pages = {823-826},
doi = {http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.93},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - BioMedical Engineering and Informatics, International Conference on
TI - A Computer Aided Diagnosis System in Mammography Using Artificial Neural Networks
SN - 978-0-7695-3118-2
SP823
EP826
A1 - Guodong Zhang,
A1 - Peiyu Yan,
A1 - Hong Zhao,
A1 - Xin Zhang,
PY - 2008
KW - computer aided diagnosis
KW - micro-calcifications
KW - mammogram
KW - classification
VL - 2
JA - BioMedical Engineering and Informatics, International Conference on
ER -
 
In this paper we present a method for developing a fully automated computer aided diagnosis (CAD) system to help radiologist in detecting and diagnosing micro-calcifications (MCCs) in digital format mammograms. One aim of the CAD system is to increase the effectiveness and efficiency of screening procedures by using computer. Another aim of the CAD is to extract and analyze the characteristics of vary lesions in an objective manner, then can improve the diagnostic accuracy and reduce the numbers of false-positive diagnoses of malignancies. Automatic segmentation, features extraction, suspicious area detection, classification and a back propagation neural network (BPNN) techniques were used in the development of this system. And the BPNN was used to classifying the marked regions into benign and malignant as the most important stage.
Index Terms:
computer aided diagnosis, micro-calcifications, mammogram, classification
Citation:
Guodong Zhang, Peiyu Yan, Hong Zhao, Xin Zhang, "A Computer Aided Diagnosis System in Mammography Using Artificial Neural Networks," bmei, vol. 2, pp.823-826, 2008 International Conference on BioMedical Engineering and Informatics, 2008
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