Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique.
Worldwide, breast cancer is the fifth most common cause of cancer death (after lung cancer, stomach cancer, liver cancer, and colon cancer). This study has focused on the developnment of Fuzzy Artmap classifier to classify breast cancer cases. Fuzzy Artmap is a class of neural network techniques th...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | eng |
| Published: |
2007
|
| Subjects: | |
| Online Access: | https://etd.uum.edu.my/70/1/ali_yahyai_alfakhi_almadani.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my-uum-etd.70 |
|---|---|
| record_format |
uketd_dc |
| institution |
Universiti Utara Malaysia |
| collection |
UUM ETD |
| language |
eng |
| topic |
Q Science (General) |
| spellingShingle |
Q Science (General) Almadani, Ali Yahyai Alfakhi Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique. |
| description |
Worldwide, breast cancer is the fifth most common cause of cancer death (after lung cancer, stomach cancer, liver cancer, and colon cancer). This study has focused on the
developnment of Fuzzy Artmap classifier to classify breast cancer cases. Fuzzy Artmap is a class of neural network techniques that capable to perform an incremental learning. This capable allow a pattern recognize system to add new information as a model even after initial learning process. This prototype has been tested with 699 cases which comprises 9 feature attributes. The prototype was developed using visual basic and MS access 2000. The result shows this prototype classification result is comparable with multilayer perceptron, with average classification result is 81.71 percent. |
| format |
Thesis |
| qualification_name |
masters |
| qualification_level |
Master's degree |
| author |
Almadani, Ali Yahyai Alfakhi |
| author_facet |
Almadani, Ali Yahyai Alfakhi |
| author_sort |
Almadani, Ali Yahyai Alfakhi |
| title |
Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique. |
| title_short |
Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique. |
| title_full |
Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique. |
| title_fullStr |
Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique. |
| title_full_unstemmed |
Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique. |
| title_sort |
diagnosing breast cancer disease using fuzzy artmap technique. |
| granting_institution |
Universiti Utara Malaysia |
| granting_department |
Sekolah Siswazah |
| publishDate |
2007 |
| url |
https://etd.uum.edu.my/70/1/ali_yahyai_alfakhi_almadani.pdf |
| _version_ |
1747826836019085312 |
| spelling |
my-uum-etd.702013-07-24T12:05:29Z Diagnosing Breast Cancer Disease Using Fuzzy Artmap Technique. 2007-11 Almadani, Ali Yahyai Alfakhi Sekolah Siswazah Graduate School Q Science (General) Worldwide, breast cancer is the fifth most common cause of cancer death (after lung cancer, stomach cancer, liver cancer, and colon cancer). This study has focused on the developnment of Fuzzy Artmap classifier to classify breast cancer cases. Fuzzy Artmap is a class of neural network techniques that capable to perform an incremental learning. This capable allow a pattern recognize system to add new information as a model even after initial learning process. This prototype has been tested with 699 cases which comprises 9 feature attributes. The prototype was developed using visual basic and MS access 2000. The result shows this prototype classification result is comparable with multilayer perceptron, with average classification result is 81.71 percent. 2007-11 Thesis https://etd.uum.edu.my/70/ https://etd.uum.edu.my/70/1/ali_yahyai_alfakhi_almadani.pdf application/pdf eng validuser masters masters Universiti Utara Malaysia Amin, A. & Murshed, N. (1999). Recognition of Printed Arabic Words with Fuzzy ARTMAP Neural Networ, IEEE, 2903-2905. Azouaoui, 0. , Ouaaz, M., Chohra, A. , Farah, A. & Achour, K. (2001). Fuzzy Artmap neural network based collision avoidance approach for autonomous robotic system, second workshop on robot motion and control. University of central Florida Electrical and Computer Engineering Department, 1-10. Aggarwal, R. K. , Xuan, Q. Y. , Johns, A. T. , Li, F. & Bennett, A. (1999). A novel approach to fault diagnosis in mulicircuit transmission lines using Fuzzy Artmap neural networks, IEEE computer society, 4-7. Bartfai, G. (1995). An improve learning algorithm for the Fuzzy Artmap neural Network. IEEE, 1-6. Brezmes, J. , Cabre, S. , Rojo, S. , Lobet, E. , Vilanova, X. & Correig, X. (2005). Discrimination Between Different Samples of Olive Oil Using Variable Selection Techniques and Modified Fuzzy Artmap Neural Networks. IEEE computer society,4-10. Carpenter, G. & Grossberg, S. (1992). Aself-organizing neural network for supervised learning, Recognition and prediction, IEEE computer society, 1-2. Christodoulou, C. G. , Huang, J. , Georgiopoulos, M. , & Liou, J. J. (1994). Application of the Artmap neural network the design cascaded gratings and frequency selective surfaces, IEEE ,4-9. Carpentert, G.A. , Grossbergt, S. ,Markuzon, N. , Reynolds, J. H , & Rosen, D.B. (1992). Fuzzy Artmap: an adaptive resonance architcture for incremental learning of analog maps, boston university. IEEE, I-11. Carpenter, G.A. , Grossberg, S. & Reynolds, J. H. (1995). A Fuzzy Artmap nonparametric probability estimator for nonstationary pattern recognition problems. IEEE transactions on neural networks, 1-4. Chen, B. & Hoberock, L.L. (1996).a Fuzzy neural network architecture for Fuzzy controand classification..IEEE,5-11 Charalampidis, D. & Kasparis, T. (1999). A Fuzzy ARTMAP Based Classification Technique of Natural Textures, Electrical and Computer Engineering, 5-8. Granger, E. , Rubin, M. A. , Grossberg, S. & Lavoie, P. (2000). Classification of incomplete data using the Fuzzy Artmap neural network. IEEE,4-8. Granger, E. , Rubin, M. A. , Grossberg, S. & lavoie, P. (2000). Classification of Incomplete Data Using the Fuzzy ARTMAP Neural Network. 2000 British Crown, 6-8. Charalampidis, D. , Kasparis, T. & Georgiopoulos, M. (2001).Classification of noisy sigals using Fuzzy Artmap neural network. IEEE computer society 6-10. Gopal, S. , Woodcock, C.E. & Strahler, H.(1996). Fuzzy Artmap classification of Global land cover from A VHRR data set. IEEE , 7-17. Henniges, P. , Granger, E. & Sabourin, R.(2005). Factors of Overtraining with Fuzzy Artmap Neural Networks. IEEE, 15-17. Ham, F.M. ,& Han, S. (1996). Classification of cardiab arrhythmias using Fuzzy Artmap, transaction on biomedical engineering, 2-5. Jervis, B. W. , Smaglo, L. & Djebali, S. (2001). The rapid classification of brain condition using neural networks, Institution of Electrical Engineers. Kuan, M. M. , Lim, C.P. , Morad, N. , & Harrison, R.F. (2000). An experimental study of original and ordered Fuzzy Artmap neural network in pattern classification tasks. IEEE computer society. Loo, C.K. &Rao, M.V.C. (2005). Accurate and reliable diagnosis and classification using probabilistic ensemble simplified Fuzzy Artmap. IEEE transaction on knowledge and data engineering, 1-5. Lerner, & Vigdor, B. (2004). An empirical study of Fuzzy Artmap applied to Cytogenetics. IEEE computer society,5-10. Lerner, B. & Vigdor, B. (2006). Image Classification for Genetic Diagnosis using Fuzzy Artmap, Ben-Gurion University. IEEE computer society, 1-IO. Le, Q. , Anagnostopoulos, G. C. , Georgiopoulos, M. & Ports, K. (2005). An Experimental Comparison of Semi-Supervised Artmap Architectures, GCS and GNG Classifiers. IEEE. 3-9. Loo, C. K. & Rao, M. V. C. (2005). Accurate and Reliable Diagnosis and Classification Using Probabilistic Ensemble Simplified Fuzzy Artmap. IEEE computer society Murshed, N. A. ,& Bortolozzi, F.(1998) A Fuzzy Artmap Module for Graphics Symbols Recognitio. IEEE, 6-10. Murshedx, N. A. , Bortolozzif, F. & Sabourinff, R. (1995). Off-line signature verification using Fuzzy Artmap neural network. IEEE computer society, 3 Ranzuhalli, P., Polikar, R., Udpa, L. & Udpa, S.S(2000). Fuzzy Artmap network with evolutionary learning. IEEE computer society,4-10. Srinivasa, N. & Ziegert, J.C.(1994). An application of Fuzzy Artmap neural network to real-time learning and prediction of time-variant machine tool error maps. University of Florida. Department of Mechanical Engineering. IEEE computer society,6-8. Silveira, M. C. G. , Lotufo, A. D. P. , Minussi, C. R. (2003). Transient stability analysis of electrical power system using a neurl network network based on fyzzy Artmap, IEEE, 11-19. Tan, S.C. & Lim, C.P. (2004). Application an adaptive neural networks with symbolic rule extraction to fault detection and diagnosis in a power generation plant. IEEE computer society, 1-5. Ranzuha1,P. , Polikar, R .& Udpa, S.S.(2000). Fuzzy Artmap Network with Evolutionary Learning. IEEE computer society, 6-10. Prateepasen, A. , Kaewtrahlpong, P. , & Jirarungsatean, C.(2004) classification of DC micro spot welding quality using Fuzzy Artmap on acoustic emissing monitoring, university of technology thonburi. IEEE ,2-10. Verzi, S. , Heileman, G. L. , Georgiopoulos, M. , Anagnostopoulos,G.C. (2003). Universal Approximation, With Fuzzy ART and. Fuzzy Artmap. IEEE computer society, 6-10. Vuskovic, M. & Du, S. (2002). Classification of prehensile EMG patterns with simplified Fuzzy Artmap nerworks. IEEE computer society, 2-4. Vaishnavi & Kuechler (2006). Design Research in information system. Retrieved June 15, 2006, from http://www.isworld.org/Researchdesign/drisISworld.htm Pomchaikajomsak, P. & Thammano, A.(2003). Handwritten thai character recognition using Fuzzy membership and Fuzzy Artmap. Information Technology, 6-9. Wool, S. C. , Lim, C. P., Osman, R.(2000). Text-Dependent Speaker Recognition Using the Fuzzy Artmap Neural Network, Universiti Sains Malaysia. School of lndustrial Technology. 6-10. Wee, C. , paramesran, R. ,takeda, F., tsuzuki, T. ,kadota, H. , shimanouchi, S. (2002). Classification of rice grains using Fuzzy Artmap neural Network. university of Malaya. Telecommunication engineering 6-10. Xuan, Q. Y. , Agganva, R. K. , Song, Y. H. , .Johns, A. T. (1997). Application of Fuzzy Artmap for fault monitoring on complex transmission system . University of Bath. School of Electronics and Electrical Engineering, 5-9. |