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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiang Yun Li Zhanhuai Wang Yong Zhang Longbo |
| Copyright Year | 2006 |
| Description | Author affiliation: Coll. of Comput. Sci., Northwestern Polytech. Univ. (Jiang Yun; Li Zhanhuai; Wang Yong; Zhang Longbo) |
| Abstract | Detecting tumor in mammography is a difficult task because of complexity in the image. This brings the necessity of creating automatic tools to find whether a mammography present tumor or not. In this paper we integrate neural network with reduction of rough set theory which we call the rough neural network (RNN) to classify digital mammography. The experimental results show that the RNN performs better than purely using neural network in terms of time, and it can get 92.37% classifying accuracy which is higher than 81.25% using neural network only |
| Sponsorship | IEEE CPS |
| Starting Page | 853 |
| Ending Page | 857 |
| File Size | 321762 |
| Page Count | 5 |
| File Format | |
| ISBN | 0769527027 |
| DOI | 10.1109/ICDMW.2006.1 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Recurrent neural networks Neural networks Feature extraction Educational institutions Mammography Breast cancer Data mining Medical diagnostic imaging Neoplasms Biomedical imaging |
| Content Type | Text |
| Resource Type | Article |
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