- Title
- Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble
- Creator
- Haque, Md Sarwar; Hassan, Md Rafiul; BinMakhashen, Galal; Owaidh, Abdullah; Kamruzzaman, Joarder
- Date
- 2018
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164635
- Identifier
- vital:13097
- Identifier
-
https://doi.org/10.1007/978-3-319-76348-4_68
- Identifier
- ISBN:2194-5357 (ISSN); 9783319763477 (ISBN)
- Abstract
- It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.
- Publisher
- Springer Verlag
- Relation
- 17th International Conference on Intelligent Systems Design and Applications, ISDA 2017; Delhi, India; 14th-16th December 2017; published in Intelligent Systems Design and Applications (part of the Advances in Intelligent Systems and Computing book series) Vol. 736, p. 702-711
- Rights
- Copyright © 2018, Springer International Publishing AG, part of Springer Nature.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Breast cancer; Breast Dense and Fatty; DCT; Machine learning tools; Pattern Recognition; PCA
- Full Text
- Reviewed
- Hits: 3160
- Visitors: 3385
- Downloads: 412
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Accepted version | 380 KB | Adobe Acrobat PDF | View Details Download |