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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yuan-Hsiang Chang Bin Zheng Xiao-Hui Wang Good, W.F. |
| Copyright Year | 1999 |
| Description | Author affiliation: Dept. of Radiol., Pittsburgh Univ., PA, USA (Yuan-Hsiang Chang) |
| Abstract | The authors investigated computer-aided diagnosis (CAD) schemes to determine the probability for the presence of breast cancer using artificial neural networks (ANNs) that were trained by a backpropagation (BP) algorithm or by a genetic algorithm (GA). A clinical database of 418 previously verified patient cases was employed and randomly partitioned into two independent sets for CAD training and testing. During training, the BP and the GA were independently applied to optimize, or to evolve the inter-connecting weights of the ANNs. Both the BP/GA-trained CAD performances were then compared using the receiver-operating characteristics (ROC) analysis. In the training set, both the BP/GA-trained CAD schemes yielded the areas under ROC curves of 0.91 and 0.93, respectively. In the testing set, both the BP/GA-trained ANNs yielded the areas under ROC curves of approximately 0.83. These results demonstrated that the GA performed slightly better, although not significantly, than BP for the training of the CAD schemes. |
| Starting Page | 3674 |
| Ending Page | 3679 |
| File Size | 570328 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780355296 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.1999.836267 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-07-10 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computer aided diagnosis Breast cancer Artificial neural networks Genetic algorithms Testing Biomedical imaging Medical diagnostic imaging Neural networks Backpropagation algorithms Databases |
| Content Type | Text |
| Resource Type | Article |
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