Conference proceeding
Information theory optimization based feature selection in breast mammography lesion classification
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Vol.2018-, pp.817-821
04/2018
DOI: 10.1109/ISBI.2018.8363697
Abstract
Quantitative imaging features of intensity, texture, and shape were extracted from breast lesions and surrounding tissue in 287 mammograms (150 malignant, 137 benign). A feature set reduction method to remove highly intra-correlated features was devised using k-medoids clustering and k-fold cross validation. A novel feature selection method using information theory was introduced which builds a feature set for classification by determining a group of class-informative features with low set co-information. An artificial neural network was built from the selected feature set using 10-hidden layer nodes and the tanh activation function. The resulting computer-aided diagnosis tool achieved a training accuracy of 96.2%, sensitivity of 97.6%, specificity of 95.2%, and area-under-the-curve of 0.971 along with 97.1% sensitivity and 94.9% specificity a blinded validation set.
Details
- Title: Subtitle
- Information theory optimization based feature selection in breast mammography lesion classification
- Creators
- Johanna Uthoff - University of IowaJessica C Sieren - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Vol.2018-, pp.817-821
- Publisher
- IEEE
- DOI
- 10.1109/ISBI.2018.8363697
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984318701502771
Metrics
13 Record Views