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Skin lesion analysis using generative adversarial networks: a review

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Abstract

Skin cancer is one of the primary causes of death in the world. Timely diagnosis of skin cancer can reduce the number of deaths. Skin cancer can be diagnosed early using deep learning-based systems. The performance of deep learning-based systems suffers from overfitting if we don’t have enough data to train them. Acquiring a large amount of skin lesion images for training a deep learning-based system is a difficult task. Overfitting can be avoided using data augmentation. Generative adversarial networks (GANs) are very popular in skin lesion tasks because of their ability to generate high-quality synthetic skin lesion images. GANs are used for the classification and segmentation of skin-lesion images. We review the most relevant papers discussing the use of GANs for augmenting skin lesion datasets in this work. We gave an overview of the most commonly used GAN architectures in skin lesion analysis.

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Acknowledgements

I want to acknowledge Dr. Hazrat Ali’s valuable input during the review process.

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  1. Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Syed Qasim Gilani & Oge Marques

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Appendix

Appendix

A list of abbreviations used in this paper is given in Table 6.

Table 6 List of abbreviations used in this paper
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1.1 Datasets used for skin lesion analysis

GANs were used for synthesizing lesion images due to the difficulties in collecting large datasets for skin lesion analysis. Skin lesion analysis is mostly performed using open-source datasets. Some of the available open-source datasets are presented in Table 7.

Table 7 Datasets for skin lesion analysis
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1.2 Github repositories

Codes are available for synthesizing skin lesion images using different architectures of GANs. Github repositories for implementing some of the GAN architectures discussed in Sections 3 and 4 are presented in Table 8.

Table 8 GitHub repositories for skin lesion analysis
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A comparison of the computational cost of different synthesis networks given in Ding et al., Sarker et al. is presented in Table 9. Table 9 compares the number of parameters in millions.

Table 9 Computational cost of different semantic synthesis method
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Gilani, S.Q., Marques, O. Skin lesion analysis using generative adversarial networks: a review. Multimed Tools Appl 82, 30065–30106 (2023). https://doi.org/10.1007/s11042-022-14267-z

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