The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
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.
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Cites
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Automated breast ultrasound lesions detection using convolutional neural networks
Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2731873 · Cited by 1,086
Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review
Jalalian, A., Mashohor, S. B. T., Mahmud, H. R., Saripan, M. I. B., Ramli, A. R. B., & Karasfi, B. (2013) · Clinical Imaging · doi:10.1016/j.clinimag.2012.09.024 · Cited by 337
Automatic breast ultrasound image segmentation: a survey
Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.012 · Cited by 284
“Breast ultrasound (BUS) imaging has become one of the most important and effective modality for the early detection of breast cancer because of its noninvasive, nonradioactive and cost-effective nature [4]; and it is most suitable for large scale breast cancer screening and diagnosis in low-resource countries and regi…”
BackgroundSelf-citationByra, M., Galperin, M., Ojeda‐Fournier, H., Olson, L., O'Boyle, M., Comstock, C., & Andre, M. (2019) · Medical Physics · doi:10.1002/mp.13361 · Cited by 253
A systematic review of artificial intelligence techniques in cancer prediction and diagnosis
Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09648-w · Cited by 223
Metadata
- Authors
- Cheng, H. D., Shan, J., Ju, W., Guo, Y., & Zhang, L.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2010-01
- Volume
- 43
- Issue
- 1
- Pages
- 299-317
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W1988819287
- Cited by
- 728
- References
- 88
- FWCI
- 25.66
- Type
- journal article
- Language
- en
- Open access
- closed
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