Name
Title | ||
|---|---|---|
Automated breast cancer detection and classification using ultrasound images: A survey |
Abstract | ||
|---|---|---|
Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2009.05.012 | Pattern Recognition |
Keywords | Field | DocType |
automated breast cancer detection and classification,ultrasound (us) imaging,early detection,cad system,feature extraction and selection,diagnosis tool,breast cancer detection,leading cause,classifiers,breast cancer,cad (computer-aided diagnosis),computer-aided diagnosis,automated breast cancer detection,ultrasound imaging,ultrasound image,beneficial mean,breast cancer control,image features,cause of death,ultrasound,feature extraction | CAD,Disease,Breast cancer,Pattern recognition,Segmentation,Feature extraction,Preprocessor,Artificial intelligence,Breast disease,Cancer,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
43 | 1 | Pattern Recognition |
Citations | PageRank | References |
71 | 3.12 | 45 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H. D. Cheng | 1 | 1900 | 138.13 |
| Juan Shan | 2 | 80 | 4.27 |
| Wen Ju | 3 | 91 | 4.80 |
| Yanhui Guo | 4 | 321 | 40.94 |
| Ling Zhang | 5 | 143 | 14.77 |