Name
Abstract | ||
|---|---|---|
One of the leading diseases in women is breast cancer. The detection in an earlier stage is done by indicating the presence of architectural distortion (AD). An AD detection system with support vector machine is developed in this research. The 15 features are extracted from the fuzzy co-occurrence matrix and fractal dimension. The principal component analysis is also implemented to help in feature redundancy reduction. We found out that the best system for the training data set yields 91.67 % correct AD classification with 0.93 sensitivity of detecting AD and 0.91 specificity of detecting true negative. The best result of the blind test mammograms is at 100.00 % correct AD classification with approximately 16 false positive areas per image. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889938 | Neural Networks |
Keywords | Field | DocType |
cancer,diseases,feature extraction,fractals,fuzzy set theory,image classification,mammography,matrix algebra,medical image processing,object detection,principal component analysis,AD classification,AD detection system,architectural distortion detection,blind test mammograms,breast cancer,diseases,feature extraction,feature redundancy reduction,fractal dimension,fuzzy co-occurrence matrix,principal component analysis,support vector machine,training data set,Architectural Distortion,Breast Cancer,Fractal Dimension,Fuzzy Co-occurrence,Spiculated Mass,Support Vector Machine | Pattern recognition,Fractal dimension,Computer science,Fuzzy logic,Support vector machine,Redundancy (engineering),Artificial intelligence,Relevance vector machine,Architectural Distortion,Principal component analysis,Machine learning,True negative | Conference |
ISSN | ISBN | Citations |
2161-4393 | 978-1-4799-6627-1 | 1 |
PageRank | References | Authors |
0.36 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Orawan Netprasat | 1 | 1 | 0.36 |
| S. Auephanwiriyakul | 2 | 246 | 39.45 |
| Nipon Theera-umpon | 3 | 184 | 30.59 |