A Review of the Automatic Methods of Cancer Detection in Terms of Accuracy, Speed, Error, and the Number of Properties (Case Study: Breast Cancer)

Authors

  • Jalilvand Farnaz

Keywords:

automatic methods of cancer detection, breast cancer, classification algorithms, vector machine algorithms, neural network algorithms, dat ining algor

Abstract

Abstract not found

How to Cite

A Review of the Automatic Methods of Cancer Detection in Terms of Accuracy, Speed, Error, and the Number of Properties (Case Study: Breast Cancer). (2016). Global Journal of Medical Research, 16(24), 23-32. https://medicalresearchjournal.org/index.php/GJMR/article/view/

References

1. http://www.bcaction.org/Pages/GetInformed/Top10Myths.html

(2007) Fact sheet No. 297: Cancer.

L. Zhaohui, W. Xiaoming, G. Shengwen, Y. Binggang (2008) Diagnosis of breast cancer tumor based on manifold learning and support vector machine Proc IEEE Int Conf. 2008, 703-707.

J. Litigate (2004) Predictive models for breast cancer susceptibility from multiple single nucleotidepolymorphisms. 10, 2725-2737.

William Anderson, Ruth Pfeiffer, Graça Dores, Mark Sherman (2006) Comparison of age distribution patterns for different histopathologic types of breast carcinoma Cancer Epidemiol Biomarkers Prev. 15(10), 1899-1905. https://doi.org/10.1158/1055-9965.epi-06-0191

Mangasarian O, Nick W, Wolberg W (1995) Breast Cancer Diagnosis and Prognosis Via Linear Programming. 43, 570-577.

D. Lawrence (1991) Handbook of genetic algorithms. 1991, 7-12.

D. Rumelhart, G. Hinton, R. Williams (1986) D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. I, D. E. Rumelhart and J. L. McClelland (Eds.) Cambridge, MA: MIT Press, pp. 1.

Kawsar Ahmed, Abdullah Al Emran, Tasnuba Jesmin, Roushney Mukti, Md Rahman, Farzana Ahmed (2013) Early Detection of Lung Cancer Risk Using Data Mining. 14, 595-598.

V. Krishnaiah (2013) Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques. 4(1), 39-45.

Bichen Zheng, Sang Yoon, Sarah Lam (2014) Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. 41(4), 1476-1482. https://doi.org/10.1016/j.eswa.2013.08.044

Hui-Ling Chen, Bo Yang, Jie Liu, Da-You Liu (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. 38(7), 9014-9022. https://doi.org/10.1016/j.eswa.2011.01.120

Jiawei Han, Micheline Kamber, Jian Pei (2006) The Morgan Kaufmann Series in Data Management Systems.

Li Ke, Nannan Mu, Yan Kang (2010) Mass computer-aided diagnosis method in mammogram based on texture features. 146-149.

Nadia Smaoui, Souhir Bessassi (2013) A developed system for melanoma diagnosis. 3(1), 10-17.

Tadashi Kondo, Junji Ueno, Schoichiro Takao (2012) Hybrid Multi-layered GMDH-Type Neural Network Using Principal Component-Regression Analysis and Its Application to Medical Image Diagnosis of Lung Cancer. 17, 20-27. https://doi.org/10.1109/biomedcom.2012.10

Rosalin Mahapatra, Babita Majhi, Minakhi Rout (2012) Reduced feature based efficient cancer classification using single layer neural network, Rosalin Mahapatraa, Babita Majhib, Minakhi Routa.

K. Jinsa Kuruvilla, Gunavathi (2013) Lung cancer classification using neural networksfor CT images.

Filippo Amato, Alberto López, Eladia Méndez, Petr Vaňhara, Aleš Hampl, Josef Havel (2013) Artificial neural networks in medical diagnosis.

Y. Irfan, P. Khan, S. Zope, Suralkar (2013) Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart disease. 2(2), 2319-5967.

C. Bishop (1995) Neural networks for pattern recognition.

Teuvo Kohonen (1996) Self-Organization and Associative Memory. 312. https://doi.org/10.1007/978-3-642-88163-3

H. Miklos (2000) Numerical control of kohonen neural network for scattered data approximation.

M. Hansen, R. Defries, J. Townshend, R. Sohlberg (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. 21(6-7), 1331-1364. https://doi.org/10.1080/014311600210209

G. Kumar, Dr Ramachandra, K. Nagamani (2013) A Research on Breast Cancer Prediction using Data Mining Techniques. 2.

A. Bellaachia, E. Guven (2006) Predicting breast cancer survivability using data mining techniques. (13), 110.

Eduardo López-Caneda, Socorro Rodríguez Holguín, Montserrat Corral, Sonia Doallo, Fernando Cadaveira (2014) Eduardo López-Caneda, Socorro Rodríguez Holguín, Montserrat Corral, Sonia Doallo, Fernando Cadaveira, Evolution of the binge drinking pattern in college students: Neurophysiological correlates. 48.

(2012) Breast cancer diagnosis on three different datasets using multi-classifiers. 2.

Dursun Delen, Glenn Walker, Amit Kadam (2005) Walker, and A Kadam, Predicting breast cancer survivability: a comparison of three data mining methods. 34(2), 113-127. https://doi.org/10.1016/j.artmed.2004.07.002

M. Messadi, M. Ammar, H. Cherifi, M. Chikh, A. Bessaid (2014) Messadi M, Ammar M, Cherifi H, Chikh MA and Bessaid A," Interpretable Aide Diagnosis System for Melanoma Recognition", 2014, Bioengineering & Biomedical Science.

A Review of the Automatic Methods of Cancer Detection in Terms of Accuracy, Speed, Error, and the Number of Properties (Case Study: Breast Cancer)

Published

2016-07-20

How to Cite

A Review of the Automatic Methods of Cancer Detection in Terms of Accuracy, Speed, Error, and the Number of Properties (Case Study: Breast Cancer). (2016). Global Journal of Medical Research, 16(24), 23-32. https://medicalresearchjournal.org/index.php/GJMR/article/view/