Name
Title | ||
|---|---|---|
Pattern recognition techniques for automatic detection of suspicious-looking anomalies in mammograms. |
Abstract | ||
|---|---|---|
We have employed two pattern recognition methods used commonly for face recognition in order to analyse digital mammograms. The methods are based on novel classification schemes, the AdaBoost and the support vector machines (SVM). A number of tests have been carried out to evaluate the accuracy of these two algorithms under different circumstances. Results for the AdaBoost classifier method are promising, especially for classifying mass-type lesions. In the best case the algorithm achieved accuracy of 76% for all lesion types and 90% for masses only. The SVM based algorithm did not perform as well. In order to achieve a higher accuracy for this method, we should choose image features that are better suited for analysing digital mammograms than the currently used ones. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.cmpb.2005.03.009 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
mass-type lesion,adaboost classifier method,face recognition,best case,lesion type,different circumstance,image feature,automatic detection,higher accuracy,digital mammograms,pattern recognition method,suspicious-looking anomaly,pattern recognition technique,machine learning,pattern recognition | Lesion types,Computer science,Computer-aided diagnosis,Artificial intelligence,Computer vision,Facial recognition system,AdaBoost,Pattern recognition,Feature (computer vision),Classification scheme,Support vector machine,Adaboost classifier,Machine learning | Journal |
Volume | Issue | ISSN |
79 | 2 | 0169-2607 |
Citations | PageRank | References |
6 | 0.49 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomasz Arodź | 1 | 58 | 7.65 |
| Marcin Kurdziel | 2 | 55 | 8.33 |
| Erik O D Sevre | 3 | 29 | 2.64 |
| David A. Yuen | 4 | 82 | 14.75 |