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http://ir.futminna.edu.ng:8080/jspui/handle/123456789/7450| Title: | Breast Cancer Classification Algorithms: A Review |
| Authors: | ABDULKADIR, N.A. MUHAMMAD, K.M. AIBINU, A.M |
| Keywords: | Artificial Neural Network, Breast Cancer, Classification, Machine Learning, Relevance Vector Machine, Support vector machine |
| Issue Date: | 8-Mar-2019 |
| Publisher: | 2019 IEEE 1st International Conference on Mechatronics, Automation and Cyber-Physical Computer System |
| Citation: | 8. N. A. Abdulkadir, M. K. Muhammad and A. M. Aibinu (2019). Breast Cancer Classification Algorithms: A Review. 2019 IEEE (Advancing Technology for Humanity) 1stInternational Conference on Mechatronic, Automation and Cyber – Physical Computer Systems, March 2019. IEEE Catalog Number: CFP19NIG-ART, Record Number: #46180, [Reference Number #: 1902225 – 001157], pp.42-48. Federal University of Technology, Owerri (FUTO), Guest House, Nigeria. http://sites.ieee.org/nigeria/tag/mac-2019/ |
| Series/Report no.: | IEEE Catalog Number: CFP19NIG-ART Record Number: #46180 [Reference Number#: 190225-001157];pp.42-48 |
| Abstract: | Review of application of some selected artificial intelligence approach for classification of breast cancer is presented in this paper. The work firstly introduces cancer, a leading cause of death worldwide responsible for over 7.4 million deaths in recent time. Then, shortly followed by brief justification for breast cancer, one of the deadliest and most frequent form of cancer. Results obtained by the application of Support Vector machine(SVM), Relevance Vector Machine(RVM) and Artificial Neural Network(ANN) in correctly classifying breast cancer upon application of the approach on one of the popularly known dataset thus ends the contribution in this paper. |
| Description: | Not Applicable |
| URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/7450 |
| Appears in Collections: | Computer Science |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| MCE_10162019 (48).pdf | 213.93 kB | Adobe PDF | View/Open |
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