research-article

Imbalanced Breast Cancer Classification Using Transfer Learning

Authors:

Amit Kumar Singh,

Anil Kumar Pandey,

Sanjay Kumar SinghAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 18, Issue 1

Pages 83 - 93

https://doi.org/10.1109/TCBB.2020.2980831

Published: 03 February 2021 Publication History

Abstract

Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact that most of the existing datasets are imbalanced, i.e., the number of instances of a particular class far exceeds that of the others. In this paper, we propose a framework based on the notion of transfer learning to address this issue and focus our efforts on histopathological and imbalanced image classification. We use the popular VGG-19 as the base model and complement it with several state-of-the-art techniques to improve the overall performance of the system. With the ImageNet dataset taken as the source domain, we apply the learned knowledge in the target domain consisting of histopathological images. With experimentation performed on a large-scale dataset consisting of 277,524 images, we show that the framework proposed in this paper gives superior performance than those available in the existing literature. Through numerical simulations conducted on a supercomputer, we also present guidelines for work in transfer learning and imbalanced image classification.

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Index Terms

Imbalanced Breast Cancer Classification Using Transfer Learning
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification

Index terms have been assigned to the content through auto-classification.

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 18, Issue 1

Jan.-Feb. 2021

395 pages

ISSN:1545-5963

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1545-5963 © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 03 February 2021

Published in TCBB Volume 18, Issue 1

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Cited By

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https://dl.acm.org/doi/10.1145/3674558.3674583
Carrasco KTomalá LRamírez Meza EMeza Bolaños DRamírez Montalvan W(2024)Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping ReviewACM Computing Surveys10.1145/364835956:8(1-38)Online publication date: 26-Apr-2024
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Kumar ATiwari HSingh RSingh ASingh S(2024)SLIDE-Net: A Sequential Modeling Approach With Adaptive Fuzzy C-Mean Empowered Data Balancing Policy for IDC DetectionIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.337353432:10(5557-5570)Online publication date: 1-Oct-2024
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Kumar ASingh SLakshmanan KSaxena SShrivastava S(2021)A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology ImagesACM Transactions on Internet Technology10.1145/342422121:2(1-22)Online publication date: 2-Jun-2021
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