Project Details
Abstract
Because breast cancer ranks in the causes of cancer deaths among women are always to occupy a high position, the early diagnosis and cure is getting more and more important. However, early diagnosis requires an accurate and reliable diagnostic procedure that allows physicians to distinguish benign breast tumors from malignant ones. Hence, we need a well-designed CAD system to assist the inexperience physicians to avoid misdiagnosis and reduce the number of benign lesion biopsies without missing cancers.
Ultrasound does not cause side effects to human, and therefore it becomes the most acceptable technique in medical imaging methods. In recent years, the practicality of 3D ultrasound imaging in breast cancer screening attracted much attention gradually. In addition to containing more structure information than traditional 2D images, the researchers can rebuild a plane from the three-dimensional structure in order to observe the organizational characteristics under different angles.
The accuracy of a CAD system is related to the data mining techniques. Nevertheless, different problems may require different parameters when applying an SVM. If parameters didn’t setting well, it will obtain unsatisfied result. On the other hand, a dataset may contain many features which may contain false correlations and hinder the process of detecting process. Without feature selection, the classification accuracy rate may be worse due to the noises or too many dirty features. In most research either parameters turning or feature selection is used to improve classification accuracy rate. Therefore, the artificial bee colony algorithm (ABC) is proposed to select the beneficial subset of features and to obtain the better parameters of SVM, which will result in a better classification.
Project IDs
Project ID:PB10107-1736
External Project ID:NSC101-2221-E182-014
External Project ID:NSC101-2221-E182-014
Status | Finished |
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Effective start/end date | 01/08/12 → 31/07/13 |
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