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Deep Learning Techniques for Breast Cancer Diagnosis: A Systematic Review

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Advances in Robotics, Automation and Data Analytics (iCITES 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1350))

Abstract

Breast cancer analysis is carried out by many computer aided diagnosis methodologies. Recently among all methodologies deep learning techniques provides better accuracy. The image segmentation, extraction of features and classification process are done automatically using deep learning techniques. Hence deep learning techniques plays an important role in analysing images. In this paper various models of deep learning used for the classification of different types of cancer such as Benign, Malignant, In-situ, Invasive Ductal Carcinoma is described. The image processing steps are explained for each and every model starting from input layer to output layer. Transfer learning techniques along with fine tuning process is also given. It also survey works which uses images from various modalities such as screen film mammography digital mammography, ultrasound, magnetic resonance imaging, digital breast tomosynthesis. This review provides an overview of all the deep learning models used for breast cancer classification of images from various modalities.

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Authors and Affiliations

  1. Kongu Engineering College, Perundurai, Erode, 638 060, India

    B. Krishnakumar & K. Kousalya

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  1. B. Krishnakumar
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  2. K. Kousalya
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Correspondence to B. Krishnakumar .

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Editors and Affiliations

  1. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Jessnor Arif Mat Jizat

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ismail Mohd Khairuddin

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Mohd Azraai Mohd Razman

  4. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ahmad Fakhri Ab. Nasir

  5. College of Engineering, Universiti Malaysia Pahang, Gambang, Malaysia

    Mohamad Shaiful Abdul Karim

  6. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Abdul Aziz Jaafar

  7. UCSI University, Kuala Lumpur, Malaysia

    Lim Wei Hong

  8. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Anwar P. P. Abdul Majeed

  9. University of York, York, UK

    Pengcheng Liu

  10. School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Hyun Myung

  11. Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Han-Lim Choi

  12. Department of Information Engineering, University of Padua, Padova, Italy

    Gian-Antonio Susto

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Krishnakumar, B., Kousalya, K. (2021). Deep Learning Techniques for Breast Cancer Diagnosis: A Systematic Review. In: Mat Jizat, J.A., et al. Advances in Robotics, Automation and Data Analytics. iCITES 2020. Advances in Intelligent Systems and Computing, vol 1350. Springer, Cham. https://doi.org/10.1007/978-3-030-70917-4_16

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