Survey of deep learning in breast cancer image analysis

  • Taye Girma DebeleeEmail author
  • Friedhelm Schwenker
  • Achim Ibenthal
  • Dereje Yohannes


Computer-aided image analysis for better understanding of images has been time-honored approaches in the medical computing field. In the conventional machine learning approach, the domain experts in medical images are mandatory for image annotation that subsequently to be used for feature engineering. However, in deep learning, a big jump has been made to help the researchers do segmentation, feature extraction, classification, and detection from raw medical images obtained using digital breast tomosynthesis, digital mammography, magnetic resonance imaging, and ultrasound imaging modalities. As a result, deep learning (DL) has gained a state-of-the-art in many application areas, for example, breast cancer image analysis. In this survey paper, we reviewed the most common breast cancer imaging modalities, public, most cited and recently updated breast cancer databases, histopathological based breast cancer image analysis, and DL application types in medical image analysis. We finally conclude by pointing out the research gaps to be addressed in the future.


Breast cancer Breast cancer databases Imaging modalities Medical image analysis Deep learning application 



The corresponding author would like to thank the Ethiopian Ministry of Education (MoE) and the Deutscher Akademischer Auslandsdienst (DAAD) for funding this research work (Funding number 57162925).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany
  2. 2.Department of Computer EngineeringAddis Ababa Science and Technology UniversityAddis AbabaEthiopia
  3. 3.HAWK University of Applied Sciences and ArtsGöttingenGermany

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