Name
Abstract | ||
|---|---|---|
The need for early detection of breast cancer has led to establishing screening programs that generate large volumes of mammograms to be analyzed. These analysis are time consuming and labor intensive. Computerized analysis of mammograms has been suggested as ''second opinion'' or ''pre-reader''. In this paper, we suggest a texture-based computerized analysis clusters of microcalcifications detected on mammograms in order to classify them into benign and malignant types. The test of the proposed system yielded a sensitivity of 100%, a specificity of 87.77% and a good classification rate of 89%; the area under the fitted ROC-curve using the MedCalc Statistical Software was 0.968. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.dsp.2011.09.004 | Digital Signal Processing |
Keywords | Field | DocType |
breast cancer,malignant type,early detection,computerized analysis,large volume,texture-based analysis,proposed system,medcalc statistical software,texture-based computerized analysis cluster,good classification rate,fitted roc-curve,neural network,texture | Early detection,Statistical software,Breast cancer,Pattern recognition,Second opinion,Artificial intelligence,Artificial neural network,Classification rate,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 1 | 1051-2004 |
Citations | PageRank | References |
7 | 0.48 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alain Tiedeu | 1 | 48 | 4.04 |
| Christian Daul | 2 | 121 | 14.78 |
| Aude Kentsop | 3 | 7 | 0.48 |
| Pierre Graebling | 4 | 99 | 8.32 |
| Didier Wolf | 5 | 11 | 1.41 |