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Deep Layer CNN Architecture for Breast Cancer Histopathology Image Detection

  • Zanariah ZainudinEmail author
  • Siti Mariyam Shamsuddin
  • Shafaatunnur Hasan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

In recent years, there are various improvements in computational image processing methods to assist pathologists in detecting cancer cells. Consequently, deep learning algorithm known as Convolutional Neural Network (CNN) has now become a popular method in the application image detection and analysis using histopathology image (images of tissues and cells). This study presents the histopathology image related to breast cancer cells detection (mitosis and non-mitosis). Mitosis is an important parameter for the prognosis/diagnosis of breast cancer. However, mitosis detection in histopathology image is a challenging problem that needs a deeper investigation. This is because mitosis consists of small objects with a variety of shapes, and is easily confused with some other objects or artefacts present in the image. Hence, this study proposed three types of deep layer CNN architecture which are called 6-layer CNN, 13-layer CNN and 17-layer CNN, respectively in detecting breast cancer cells using histopathology image. The aim of this study is to detect the breast cancer cell which is called mitosis from histopathology image using suitable layer in deep layer CNN with the highest accuracy and True Positive Rate (TPR), and the lowest False Positive Rate (FPR) and loss performances. The result shows a promising performance for deep layer CNN architecture of 17-layer CNN is suitable for this dataset with the highest average accuracy, 84.49% and True Positive Rate (TPR), 80.55%; while the least False Positive Rate (FNR), 11.66% and loss 15.50%.

Keywords

Breast cancer image detection Deep Learning Histopathology image Convolutional Neural Network (CNN) 

Notes

Acknowledgement

This work is supported by Ministry of Education (MOE), Malaysia, Universiti Teknologi Malaysia (UTM), Malaysia and ASEAN-Indian Research Grant. This paper is financially supported by MYBRAIN, Grant No. 17H62, 03G91, and 04G48. The authors would like to express their deepest gratitude to the Bram van Ginneken, SjoerdKerkstra, and James Meakin for their support in providing the MITOS-ATYPHIA datasets to ensure the success of this research.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zanariah Zainudin
    • 1
    Email author
  • Siti Mariyam Shamsuddin
    • 1
  • Shafaatunnur Hasan
    • 1
  1. 1.School of Computing, Faculty of EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia

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