Abstract
In real-world classification tasks, it is common to find class imbalance issues in the training datasets, i.e. an unequal number of examples among the different classes. The class imbalance problem biases the performance of predictive models by overlooking minority classes; this is because predictive models employ learning rules with accuracy-based cost functions, thus favoring majority classes. In this work, the class imbalance issue is tackled through generative models, using the BreakHis dataset, a histopathologic image set intended for breast cancer classification, as a case study. The BreasHis’ minority class is balanced by adding synthetic images obtained by means of different generative methods, including variational autoencoders and two different generative adversarial networks. The quality of the image sets created by the different generative models, and their effects in balancing the BreakHis dataset, are evaluated through several quantitative metrics computed from classification tasks. Statistical analysis is performed and the results indicate that the DCGAN network is superior to the other evaluated models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Latif, J., Xiao, C., Imran, A., Tu, S.: Medical imaging using machine learning and deep learning algorithms: a review, pp. 1–5 (2019)
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63, 1455–1462 (2016)
Benhammou, Y., Achchab, B., Herrera, F., Tabik, S.: BreakHis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights. Neurocomputing 375, 9–24 (2020)
Langr, J., Bok, V.: GANs in action: deep learning with generative adversarial networks. Manning (2019)
Foster, D.: Generative Deep Learning. O’Reilly Media (2022)
Kingma, D.P., Welling, M.: Auto-encoding varational Bayes (2013). arXiv:1312.6114
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Cornell University (2015). http://export.arxiv.org/pdf/1511.06434
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63, 139–144 (2020)
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation (2017)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, pp. 448–456. pmlr (2015)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization (2016). arXiv:1607.08022
Tan, M., Le, Q.V.: Efficientnet: rethinking model scaling for convolutional neural networks (2019)
Glassner, A.: Deep Learning: A Visual Approach. No Starch Press (2021)
Zhao, S., Song, J., Ermon, S.: Towards deeper understanding of variational autoencoding models (2017). arXiv:1702.08658
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Acknowledgements
This work was partially supported by the National Council of Humanities, Science and Technology (CONAHCYT) of Mexico, via Postgraduate Scholarship 824473 (A. Rosales) and Grant CÁTEDRAS-2598 (A. Rojas).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rosales-Morales, A.E. et al. (2024). Generative Models for Class Imbalance Problem on BreakHis Dataset: A Case Study. In: Castillo, O., Melin, P. (eds) New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics. Studies in Computational Intelligence, vol 1149. Springer, Cham. https://doi.org/10.1007/978-3-031-55684-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-55684-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-55683-8
Online ISBN: 978-3-031-55684-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)