Location of Repository

Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier

By Kanaan A Faisal

Abstract

This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classi- fiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time

Topics: Computer
Year: 2006
OAI identifier: oai:generic.eprints.org:14853/core450
Provided by: KFUPM ePrints

Suggested articles

Preview

Citations

  1. (2002). A comparison of methods for multi-class support vector machines. doi
  2. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. doi
  3. (2002). Applied multivariate statistical analysis (5th ed.). Upper Saddle River,
  4. (1999). Associations statistical, mathematical and neural approaches for mining breast cancer patterns. Expert Systems with doi
  5. Computer Manual in MATLAB to Accompany Pattern Classification.
  6. (2004). Functional Networks as a New Framework for the Pattern Classifcation Problems.
  7. (1997). Fuzzy logic based approach for semilogical analysis of microcalcification in mammographic images. doi
  8. (1997). Fuzzy logic in computer-aided breast-cancer diagnosis: analysis of lobulation. doi
  9. (1998). Introduction to Functional Networks With Applications, A Neural Based Paradigm. doi
  10. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology, in: doi
  11. (1998). Neural Networks approach to early breast cancer detection. Systems Architecture, doi
  12. (1984). Prebiopsy localization of nonpalpable breast lesions. doi
  13. (1993). Progress in supervised neural networks: What’s new since Lippmann?”,
  14. (2005). The Maximum Likelihood Functional Networks as a Novel Approach for Pattern Classifcation Problems. Journal of Neurocomputing.
  15. (1991). Theprevalenceofcarcinomainpalpablevsimpalpable, mammographically detected lesions. doi
  16. (1997). Use of fine needle aspiration for solid breast lesions is accurate and costeffective. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.