Name
Abstract | ||
|---|---|---|
Recently, Digital mammogram has become one of the most effective techniques for early breast cancer detection. The aim of this study is to develop an automated system for digital mammogram analysis. In the proposed system, the regions of interest (ROIs) in the mammogram are firstly segmented by a topographic representation method called the is contour map. Subsequently, the textural, intensity and shape features are extracted from the ROIs. Then an optimal feature selection method (Correlation-based Feature Selection, CFS) is used to select some important features to classify the ROIs as either masses or non-masses. Finally, we use these selected features to train the cost-sensitive BP neural network. The experimental results show that the proposed method can produce better identification performance than some other algorithms. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TrustCom.2011.246 | IEEE International Conference on Trust, Security and Privacy in Computing and Communications |
Keywords | Field | DocType |
isocoutour map,feature extracted,optimal feature selection,classification | Computer vision,Feature selection,Pattern recognition,Image texture,Computer science,Contour line,Image segmentation,Feature extraction,Artificial intelligence,Backpropagation,Contextual image classification,Artificial neural network | Conference |
Volume | Issue | ISSN |
null | null | 2324-898X |
Citations | PageRank | References |
1 | 0.48 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenfeng Han | 1 | 1 | 0.48 |
| Jianwei Dong | 2 | 2 | 1.86 |
| Yu-Ting Guo | 3 | 4 | 1.97 |
| Ming Zhang | 4 | 89 | 18.62 |
| Jianzhong Wang | 5 | 1 | 0.48 |