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(D1), 23-32. https://medicalresearchjournal.org/index.php/GJMR/article/view/1115

References

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

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

W Anderson, R Pfeiffer, G Dores, M Sherman (2006) Comparison of age distribution patterns for different histopathologic types of breast carcinoma. 15(10), 1899-1905.

O Mangasarian, Nick Street, W Wolberg, W (1995) Breast cancer diagnosis and prognosis via linear programming. 43, 570-577.

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

David Rumelhart, Geoffrey Hinton, Ronald Williams (1986) (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. 318-362. 1, 675-695.

Kawsar Ahmed, Abdullah Al Emran, Tasnuba Jesmin, Fatima Roushney, Md Mukti, Farzana Zamilur Rahman, Ahmed (1995) Early Detection of Lung 21.

T Kohonen (1996) Self-organization and Associative Memory. 312.

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.

G Kumar, Dr Ramachandra, K Nagamani (2013) A Research on Breast Cancer Prediction using Data Mining Techniques. 8(11S2), 362-370.

A Bellaachia, E Guven (2006) Predicting breast cancer survivability using data mining techniques. 58(13), 110.

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

Gouda Salama, M Abdelhalim, Magdy Abd-Elghany Zeid (2012) Experimental comparison of classifiers for breast cancer diagnosis. 32, 180-185.

D Delen, G Walker, A Kadam (2005) Predicting breast cancer survivability: a comparison of three data mining methods. 34(2), 113-127.

M Messadi, M Ammar, H Cherifi, M Chikh, A Bessaid (2014) Interpretable Aide Diagnosis System for Melanoma Recognition.

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

2016-09-20

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(D1), 23-32. https://medicalresearchjournal.org/index.php/GJMR/article/view/1115