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Breast Cancer Prediction on BreakHis Dataset Using Deep CNN and Transfer Learning Model

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Data Engineering for Smart Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 238))

Abstract

Breast cancer (BC) is a type of disease where cells grow uncontrollably and attack on normal cells. Death rate caused by BC can be reduced by early recognition of the disease. In the recent years, convolution neural network and its various architectures that support transfer learning showed significant improvement in the classification of malignant and non-malignant tumors. In this study, we have performed performance analysis on deep CNN and four popular CNN-based architectures: VGG16, VGG19, MobileNet, and ResNet 50 on publically available BreakHis dataset for breast cancer classification on histopathological images. Among classifiers, VGG16 has best performed with highest 94.67% accuracy, 92.60% precision, 85.21% f1-score, and 80.52% recall value.

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Authors and Affiliations

  1. Manipal University Jaipur, Jaipur, India

    Pinky Agarwal, Anju Yadav & Pratistha Mathur

Authors
  1. Pinky Agarwal
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  2. Anju Yadav
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  3. Pratistha Mathur
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Corresponding author

Correspondence to Pratistha Mathur .

Editor information

Editors and Affiliations

  1. School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW, Australia

    Priyadarsi Nanda

  2. Manipal University Jaipur, Jaipur, Rajasthan, India

    Vivek Kumar Verma

  3. Manipal University Jaipur, Jaipur, Rajasthan, India

    Sumit Srivastava

  4. Manipal University Jaipur, Jaipur, Rajasthan, India

    Rohit Kumar Gupta

  5. Department of Computer Science and Engineering, Malviya National Institute of Technology, Jaipur, Rajasthan, India

    Arka Prokash Mazumdar

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Agarwal, P., Yadav, A., Mathur, P. (2022). Breast Cancer Prediction on BreakHis Dataset Using Deep CNN and Transfer Learning Model. In: Nanda, P., Verma, V.K., Srivastava, S., Gupta, R.K., Mazumdar, A.P. (eds) Data Engineering for Smart Systems. Lecture Notes in Networks and Systems, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2641-8_8

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