Mammogram classification using back-propagation neural networks and texture feature descriptors
作者： Maria Perez , Marco E. Benalcazar , Eduardo Tusa , Wilmer Rivas , Aura Conci
DOI: 10.1109/ETCM.2017.8247515
关键词: Set (abstract data type) 、 Pattern recognition 、 Mammography 、 Feature extraction 、 Data set 、 Identification (information) 、 Breast cancer 、 Pattern recognition (psychology) 、 Artificial neural network 、 Artificial intelligence 、 Computer science
摘要: Breast cancer has an important incidence in women worldwide. Early diagnosis of this illness plays a key role decreasing its mortality and improves prognosis. Currently, mammography is considered as the standard examination for detection breast cancer. However, identification abnormalities classification masses on mammographic images are not trivial tasks dense breasts, challenge artificial intelligence pattern recognition. This work presents preliminary results automatic mammographies by texture characterization based mainly Haralick's descriptors. We implement neural network (ANN) three classes: normal, benign using leave one out technique. The set training testing ANN, taken from Digital Database Screening Mammography (DDSM). Results show that percentage correct occurs average 84.72% data set.
sci-hub.se 参考文章(38)
D. Kopans, K. Bowyer, R. Moore, M. Heath, THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY ,(2007)
Mellisa Pratiwi, Alexander, Jeklin Harefa, Sakka Nanda, Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network Procedia Computer Science. ,vol. 59, pp. 83- 91 ,(2015) , 10.1016/J.PROCS.2015.07.340
Jacques Ferlay, Isabelle Soerjomataram, Rajesh Dikshit, Sultan Eser, Colin Mathers, Marise Rebelo, Donald Maxwell Parkin, David Forman, Freddie Bray, Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer. ,vol. 136, ,(2015) , 10.1002/IJC.29210
Manish H. Bharati, J.Jay Liu, John F. MacGregor, Image texture analysis: methods and comparisons Chemometrics and Intelligent Laboratory Systems. ,vol. 72, pp. 57- 71 ,(2004) , 10.1016/J.CHEMOLAB.2004.02.005
A.K. Jain, Jianchang Mao, K.M. Mohiuddin, Artificial neural networks: a tutorial computational science and engineering. ,vol. 29, pp. 31- 44 ,(1996) , 10.1109/2.485891
S. Arivazhagan, L. Ganesan, S. Padam Priyal, Texture classification using Gabor wavelets based rotation invariant features Pattern Recognition Letters. ,vol. 27, pp. 1976- 1982 ,(2006) , 10.1016/J.PATREC.2006.05.008
Loris Nanni, Alessandra Lumini, Sheryl Brahnam, Survey on LBP based texture descriptors for image classification Expert Systems With Applications. ,vol. 39, pp. 3634- 3641 ,(2012) , 10.1016/J.ESWA.2011.09.054
Xiaoming Liu, Jinshan Tang, Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method IEEE Systems Journal. ,vol. 8, pp. 910- 920 ,(2014) , 10.1109/JSYST.2013.2286539
Robert M. Haralick, K. Shanmugam, Its'Hak Dinstein, Textural Features for Image Classification IEEE Transactions on Systems, Man, and Cybernetics. ,vol. SMC-3, pp. 610- 621 ,(1973) , 10.1109/TSMC.1973.4309314
Chenhui Shao, Kamran Paynabar, Tae Hyung Kim, Jionghua (Judy) Jin, S. Jack Hu, J. Patrick Spicer, Hui Wang, Jeffrey A. Abell, Feature selection for manufacturing process monitoring using cross-validation Journal of Manufacturing Systems. ,vol. 32, pp. 550- 555 ,(2013) , 10.1016/J.JMSY.2013.05.006