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Smart GAN: a smart generative adversarial network for limited imbalanced dataset

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Abstract

Advancements in Machine Learning (ML) and Computer Vision have led to notable improvements in the detection of breast cancer. However, the accuracy of the classifier is limited due to imbalanced datasets that cause overfitting. Thus, additional images are needed to improve the classifier’s performance. Generative Adversarial Networks (GAN) are used for image augmentation. Still, its limitations, such as the random use of different types of GAN, can lead to a too-good discriminator, causing generator training to fail due to vanishing gradients. Therefore, selecting the appropriate GAN model for the given scenario is crucial for optimal performance. This paper proposes a novel Smart Generative Adversarial Network (Smart GAN) architecture to develop an efficient and computational classification model for a limited imbalanced dataset. Smart GAN uses a three-fold approach. Different types of GAN augment the dataset in the first phase of experimental work, and their evaluation metrics are calculated. In the second phase, the metric scores are used as rewards for the Reinforcement learning model (Q-learning approach). The best augmentation is chosen based on the best Q-values for each metric score. It compares three different Convolutional Neural Networks (CNN) and selects the best-suited network to classify the augmented datasets. The proposed Smart GAN architecture outperforms other existing approaches by giving a better accuracy of 89.62% and 89.91% on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) augmented datasets, respectively, representing an approximately 10% increment in detection rate compared to non-augmented datasets.

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Data availibility

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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The custom code developed for this study is available from the corresponding author on reasonable request.

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Author notes

  1. S. K. Vyshnavi, Rupsa Dhar, B. S. A. S. Rajita, Subhrakanta Panda and Jabez Christopher have contributed equally to this work.

Authors and Affiliations

  1. CSIS department, BITS Pilani, Hyderabad Campus, Shameerpet, Hyderabad, Telangana, 500078, India

    Deepa Kumari, S. K. Vyshnavi, Rupsa Dhar, B. S. A. S. Rajita, Subhrakanta Panda & Jabez Christopher

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  1. Deepa Kumari
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Contributions

Deepa Kumari involved in the study design, data collection, and manuscript writing; Vyshnavi SK and Rupsa Dhar conducted data analysis and interpretation; BSAS Rajita compared the proposed work with existing methods; Subhrakanta Panda and Jabez Christopher supervised, reviewed and edited the manuscript.

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Correspondence to Deepa Kumari.

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Kumari, D., Vyshnavi, S.K., Dhar, R. et al. Smart GAN: a smart generative adversarial network for limited imbalanced dataset. J Supercomput 80, 20640–20681 (2024). https://doi.org/10.1007/s11227-024-06198-3

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