Fuzzy Cluster Based Diagnosis System for Digital Mammogram


The KIPS Transactions:PartB , Vol. 16, No. 2, pp. 165-172, Apr. 2009
10.3745/KIPSTB.2009.16.2.165,   PDF Download:

Abstract

According to the American Cancer Society, breast cancer is the second largest cause of cancer deaths and most frequently diagnosed cancer in women. The currently most popular method for early detection of breast cancer is the digital mammography. A mass or calcification lesion has been known as the most important clue for the diagnosis. In this paper, we propose a diagnosis approach based on fuzzy cluster knowledge base. We combine different two sources of feature data in duel OFUN-NET and produce the diagnosis result with possibility degree. We also present the experimental results on the dataset of mass and calcification lesions extracted from the public real world mammogram database DDSM. These results show higher classification accuracy than conventional methods and the feasibility as a decision supporting tool for diagnosis of digital mammogram.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
H. S. Rhee and S. M. Yoon, "Fuzzy Cluster Based Diagnosis System for Digital Mammogram," The KIPS Transactions:PartB , vol. 16, no. 2, pp. 165-172, 2009. DOI: 10.3745/KIPSTB.2009.16.2.165.

[ACM Style]
Hyun Sook Rhee and Seok Min Yoon. 2009. Fuzzy Cluster Based Diagnosis System for Digital Mammogram. The KIPS Transactions:PartB , 16, 2, (2009), 165-172. DOI: 10.3745/KIPSTB.2009.16.2.165.