Name
Title | ||
|---|---|---|
Effective Classification of Microcalcification Clusters Using Improved Support Vector Machine with Optimised Decision Making |
Abstract | ||
|---|---|---|
Classification of micro calcification clusters is very essential for early detection of breast cancer from mammograms. In this paper, an improved support vector machine (SVM) scheme is proposed, where optimized decision making is introduced for effective and more accurate data classification. Experimental results on the well-known DDSM database have shown that the proposed method can significantly increase the performance in terms of F1 and Az measurements for the successful classification of clustered micro calcifications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIG.2013.84 | ICIG |
Keywords | Field | DocType |
early detection,successful classification,az measurement,breast cancer,improved support vector machine,improved support,micro calcifications,effective classification,vector machine,accurate data classification,micro calcification cluster,optimised decision,feature extraction,cancer,support vector machine,support vector machines,image classification,kernel | Kernel (linear algebra),Object detection,Data mining,Microcalcification,Pattern recognition,Computer science,Computer-aided diagnosis,Support vector machine,Feature extraction,Artificial intelligence,Data classification,Contextual image classification | Conference |
Citations | PageRank | References |
1 | 0.38 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinchang Ren | 1 | 1144 | 88.54 |
| Zheng Wang | 2 | 84 | 8.26 |
| Meijun Sun | 3 | 74 | 11.77 |
| John Soraghan | 4 | 2 | 1.07 |