Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms

  • Wajeeha Ansar
  • Ahmad Raza Shahid
  • Basit RazaEmail author
  • Amir Hanif Dar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1187)


Breast cancer is the major cause of death among women. The best and most efficient approach for controlling cancer progression is early detection and diagnosis. As opposed to biopsy, mammography helps in early detection of cancer and hence saves lives. Mass classification in mammograms remains a major challenge and plays a vital role in helping radiologists in accurate diagnosis. In this work, we propose a MobileNet based architecture for early breast cancer detection and further classify mass into malignant and benign. It requires less memory space and provides faster computations with 86.8% and 74.5% accurate results for DDSM and CBIS-DDSM, respectively. We have achieved better results than other deep CNN models such as AlexNet, VGG16, GoogleNet, and ResNet.


Breast cancer Mammography Deep learning MobileNet Localization 



This work has been supported by Higher Education Commission under Grant # 2(1064) and is carried out at Medical Imaging and Diagnostics (MID) Lab at COMSATS University Islamabad, under the umbrella of National Center of Artificial Intelligence (NCAI), Pakistan.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wajeeha Ansar
    • 1
  • Ahmad Raza Shahid
    • 1
  • Basit Raza
    • 1
    Email author
  • Amir Hanif Dar
    • 1
  1. 1.Medical Imaging and Diagnostic (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer ScienceCOMSATS University Islamabad (CUI)IslamabadPakistan

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