Name
Abstract | ||
|---|---|---|
Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. In this work we propose for the first time the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The proposed method is developed and evaluated using a database of 76 mammograms containing 1120 MCs. To evaluate detection performance, free-response receiver operating characteristic (FROC) curves are used. Experimental results demonstrate that, when compared to several other existing methods, the proposed SVM framework offers the best performance. |
Year | DOI | Keywords |
|---|---|---|
2002 | 10.1109/ISBI.2002.1029228 | cancer,learning automata,mammography,medical image processing,breast cancer,detection performance evaluation,digitized mammograms,free-response receiver operating characteristic curves,mammograms database,medical diagnostic imaging,microcalcifications detection,small bright spots,supervised-learning problem,support vector machine learning |
Field | DocType | ISBN |
Computer vision,Mammography,Receiver operating characteristic,Learning automata,Pattern recognition,Microcalcification,Computer science,Support vector machine,Artificial intelligence,Machine learning | Conference | 0-7803-7584-X |
Citations | PageRank | References |
9 | 0.97 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Issam El-Naqa | 1 | 528 | 36.31 |
| Yongyi Yang | 2 | 1409 | 140.74 |
| Miles N. Wernick | 3 | 595 | 61.13 |
| Nikolas P. Galatsanos | 4 | 632 | 52.16 |
| Robert M Nishikawa | 5 | 599 | 58.25 |
| El-Naqa, I. | 6 | 9 | 0.97 |