Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach

Journal of Biomedical Informatics - Tập 49 - Trang 45-52 - 2014
J. Dheeba1, Nikita Singh2, S. Tamil Selvi3
1Dept. of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Kanyakumari District, Thuckalay, Tamil Nadu 629 180, India
2BSNL Nagercoil, India#TAB#
3Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti, India

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Tài liệu tham khảo

Jemal, 2011, Global cancer statistics, CA Cancer J Clin, 61, 69, 10.3322/caac.20107

Forouzanfar, 2011, Breast and cervical cancer in 187 countries between 1980 and 2010: a systematic analysis, Lancet, 378, 1461, 10.1016/S0140-6736(11)61351-2

Kolb, 2002, Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations, Radiology, 225, 165, 10.1148/radiol.2251011667

Cheng, 2003, Computer-aided detection and classification of microcalcifications in mammograms: a survey, Pattern Recogn, 36, 2967, 10.1016/S0031-3203(03)00192-4

Giger, 2001, Computer aided diagnosis in medical imaging, IEEE Trans Med Imaging, 20, 1205, 10.1109/TMI.2001.974915

Cheng, 2006, Approaches for automated detection and classification of masses in mammograms, Pattern Recogn, 39, 646, 10.1016/j.patcog.2005.07.006

Metz, 2001, ROC methodology in radiologic imaging, Invest Radiol, 21, 720, 10.1097/00004424-198609000-00009

Motakis, 2009, Data-driven approach to predict survival of cancer patients, IEEE Eng Med Biol Mag, 28, 58, 10.1109/MEMB.2009.932937

Bird, 1992, Analysis of cancers missed at screening mammography, Radiology, 184, 613, 10.1148/radiology.184.3.1509041

Baker, 2003, Computer-Aided Detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion, Am J Roentgenol, 181, 1083, 10.2214/ajr.181.4.1811083

Chan, 1990, Improvement in radiologists detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis, Invest Radiol, 25, 1102, 10.1097/00004424-199010000-00006

Hindle William H. Breast care: a clinical guidebook for women’s primary health care providers. Springer Verlag New York Inc; 1999.

Karahaliou, 2008, Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications, IEEE Trans Inf Technol Biomed, 12, 731, 10.1109/TITB.2008.920634

Kupinski, 1998, Automated seeded lesion segmentation on digital mammograms, IEEE Trans Med Imaging, 17, 510, 10.1109/42.730396

Sahiner, 1996, Classification of mass and normal breast tissue – a convolution neural network classifier with spatial domain and texture images, IEEE Trans Med Imaging, 15, 598, 10.1109/42.538937

Eltonsy, 2007, A concentric morphology model for the detection of masses in mammography, IEEE Trans Med Imaging, 26, 880, 10.1109/TMI.2007.895460

Zheng, 1996, Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications, IEEE Trans Med Imaging, 15, 589, 10.1109/42.538936

Yu, 2010, Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features, Exp Syst Appl, 37, 5461, 10.1016/j.eswa.2010.02.066

Sutton, 1972, Texture measures for automatic classification of pulmonary disease, IEEE Trans Comput, C-21, 667, 10.1109/T-C.1972.223572

Wang, 1998, Detection of microcalcifications in digital mammograms using wavelets, IEEE Trans Med Imaging, 17, 498, 10.1109/42.730395

Brem, 2007, Clinical versus research approach to breast cancer detection with CAD: where are we now?, Am J Roentgenol, 188, 234, 10.2214/AJR.06.1449

Christodoulou, 2003, Multifeature texture analysis for the classification of clouds in satellite imagery, IEEE Trans Geosci Remote Sens, 41, 2662, 10.1109/TGRS.2003.815404

Gonzales RC, Woods. Digital image processing, 2nd ed. Upper Saddle River, NJ: Prentice Hall; 2002.

Laws KJ. Texture energy measures. In: Proceeding DARPA image understanding workshop; 1979. p. 47–51.

Kocur, 1996, Using neural networks to select wavelet features for breast cancer diagnosis, Eng Med Biol Mag IEEE, 15, 95, 10.1109/51.499766

Maglogiannis, 2008, Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images, IEEE Trans Inf Technol Biomed, 12, 42, 10.1109/TITB.2006.888702

Sun, 2011, Combined feature selection and cancer prognosis using support vector machine regression, IEEE/ACM Trans Comput Biol Bioinform, 8, 1671, 10.1109/TCBB.2010.119

Zhang, 1992, Wavelet networks, IEEE Trans Neural Networks, 3, 889, 10.1109/72.165591

Sadri, 2013, Segmentation of Dermoscopy images using wavelet networks, IEEE Trans Biomed Eng, 60, 1134, 10.1109/TBME.2012.2227478

Zhang, 1995, Wavelet neural networks for function learning, IEEE Trans Sig Process, 43, 1485, 10.1109/78.388860

Obuchowski, 2003, Receiver operating characteristics curves and their use in radiology, Radiology, 229, 3, 10.1148/radiol.2291010898

Kennedy, 1995, Particle swarm optimization, Proc IEEE Conf Neural Networks, 4, 1942, 10.1109/ICNN.1995.488968

Zhou, 2002

Huo, 2001, Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis, IEEE Trans Med Imaging, 20, 1285, 10.1109/42.974923

Hupse, 2009, Use of normal tissue context in computer aided detection of masses in mammograms, IEEE Trans Med Imaging, 28, 2033, 10.1109/TMI.2009.2028611

Youden, 1950, An index for rating diagnostic tests, Cancer, 3, 32, 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3

Dheeba, 2012, A swarm optimized neural network system for classification of microcalcification in mammograms, J Med Syst, 36, 3051, 10.1007/s10916-011-9781-3

Dheeba, 2012, An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network, J Med Syst, 36, 3223, 10.1007/s10916-011-9813-z