Multi-scale CNN based on region proposals for efficient breast abnormality recognition-Reference-Cited by-同舟云学术

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Multi-scale CNN based on region proposals for efficient breast abnormality recognition

Published:2018-06-22 Issue:10 Volume:78 Page:12939-12960
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Bakkouri Ibtissam^ORCID,Afdel Karim

Funder

Moroccan Ministry of National Education, Vocational Training, Higher Education and Scientific Research

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/content/pdf/10.1007/s11042-018-6267-z.pdf

Reference52 articles.

1. Abeyratne U, Kinouchi Y, Oki H, Okada J, Shichijo F, Matsumoto K (1991) Artificial neural networks for source localization in the human brain. Brain Topogr 4:3–21. https://doi.org/10.1007/bf01129661

2. Aminikhanghahi S, Shin S, Wang W, Jeon S, Son S (2016) A new fuzzy Gaussian mixture model (FGMM) based algorithm for mammography tumor image classification. Multimed Tools Appl 76:10191–10205. https://doi.org/10.1007/s11042-016-3605-x

3. Aslan F, Yuce B, Oztas Z, Ates H (2017) Evaluation of the learning curve of non-penetrating glaucoma surgery. International Ophthalmology. https://doi.org/10.1007/s10792-017-0691-3

4. Bakkouri I, Afdel K (2017) Breast tumor classification based on deep convolutional neural networks. In: International Conference on advanced technologies for signal and image processing (ATSIP). https://doi.org/10.1109/atsip.2017.8075562

5. Bodai B, Tuso P (2015) breast cancer survivorship: a comprehensive review of long-term medical issues and lifestyle recommendations. The Permanente Journal. https://doi.org/10.7812/tpp/14-241

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