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Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12415))

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Abstract

Early diagnosis of breast cancer is the most reliable and practical approach to mitigate cancer. Computer-aided detection or computer-aided diagnosis is one of the software technologies designed to assist doctors in detecting or diagnosing cancer and to reduce mortality using medical image analysis. Recently, Convolution Neural Networks became very popular in medical image analysis helping to process vast amount of data to detect and classify cancer in a fast and efficient manner. In this paper, we implemented deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast cancer in histopathological images. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images from the public dataset BreakHis. The highest average accuracy achieved for binary classification of benign or malignant cases was 98.73% for ResNet18, followed by 97.65% for ShuffleNet and 97.44% for Inception-V3Net.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, H3G 1M8, Canada

    Abdulrahman Aloyayri & Adam Krzyżak

  2. Faculty of Computer Science Department, Saudi Electronic University, Riyadh, Saudi Arabia

    Abdulrahman Aloyayri

Authors
  1. Abdulrahman Aloyayri
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  2. Adam Krzyżak
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Corresponding author

Correspondence to Abdulrahman Aloyayri .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Aloyayri, A., Krzyżak, A. (2020). Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_45

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  • DOI: https://doi.org/10.1007/978-3-030-61401-0_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61400-3

  • Online ISBN: 978-3-030-61401-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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