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<p>Chest diseases, including COVID-19, have caused a global pandemic, resulting in a large number of deaths. In some countries, the medical system has become overwhelmed with a shortage of doctors and medical supplies, making it difficult to accommodate all patients. Deep learning has been widely used to offer smart solutions using medical images, such as chest X-rays (CXR) to identify the disease. However, it has been noticed that the majority of current studies have been based on relatively small datasets of medical images, due to the recent emergence of chest disease and the ongoing process of gathering and publishing corresponding datasets. This limited number of COVID-19 medical images may be insufficient to build robust and accurate deep learning models. To address this problem, this study proposed GANCHEST, a framework that generates Chest CXR images based on two different generative adversarial networks (GANs): the basic GAN (GAN) and the conditional GAN (CGAN). The generated images are then validated automatically through four deep transfer learning models, namely GoogleNet, InceptionV3, SqueezNet, and VGG16. The two GAN architectures' images serve as the basis for training the models, and a test set of actual chest images serves to evaluate their performance. According to the experiments, the CGAN outperformed the GAN in creating images that were more similar to the original images. Specifically, the highest classification accuracy of the CGAN achieved 92.47\% with the VGG16 model, while the highest classification accuracy of the GAN achieved 69.27\%. The GANCHEST framework, as proposed in this study, can have a wider range of applications beyond chest CXR images. It can be applied to other domains that lack datasets, such as other medical imaging modalities, where the same problem of limited dataset availability exists. The framework can be adapted to generate synthetic images that can be used to augment existing datasets and improve the performance of deep learning models. Additionally, the proposed approach of using deep transfer learning models for validation can also be applied to other fields where the need for efficient and accurate image validation arises. The GANCHEST framework is a novel approach that can be useful for several fields where the lack of datasets is a bottleneck for the performance of machine learning models.</p>

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GANCHEST: a multi-GAN-based framework for chest CXR image generation and validation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Amal A. Al-Shargabi,
  • Jowharah F. Alshobaili,
  • Abdulatif Alabdulatif,
  • Naseem Alrobah,
  • Dina M. Ibrahim

摘要

Chest diseases, including COVID-19, have caused a global pandemic, resulting in a large number of deaths. In some countries, the medical system has become overwhelmed with a shortage of doctors and medical supplies, making it difficult to accommodate all patients. Deep learning has been widely used to offer smart solutions using medical images, such as chest X-rays (CXR) to identify the disease. However, it has been noticed that the majority of current studies have been based on relatively small datasets of medical images, due to the recent emergence of chest disease and the ongoing process of gathering and publishing corresponding datasets. This limited number of COVID-19 medical images may be insufficient to build robust and accurate deep learning models. To address this problem, this study proposed GANCHEST, a framework that generates Chest CXR images based on two different generative adversarial networks (GANs): the basic GAN (GAN) and the conditional GAN (CGAN). The generated images are then validated automatically through four deep transfer learning models, namely GoogleNet, InceptionV3, SqueezNet, and VGG16. The two GAN architectures' images serve as the basis for training the models, and a test set of actual chest images serves to evaluate their performance. According to the experiments, the CGAN outperformed the GAN in creating images that were more similar to the original images. Specifically, the highest classification accuracy of the CGAN achieved 92.47\% with the VGG16 model, while the highest classification accuracy of the GAN achieved 69.27\%. The GANCHEST framework, as proposed in this study, can have a wider range of applications beyond chest CXR images. It can be applied to other domains that lack datasets, such as other medical imaging modalities, where the same problem of limited dataset availability exists. The framework can be adapted to generate synthetic images that can be used to augment existing datasets and improve the performance of deep learning models. Additionally, the proposed approach of using deep transfer learning models for validation can also be applied to other fields where the need for efficient and accurate image validation arises. The GANCHEST framework is a novel approach that can be useful for several fields where the lack of datasets is a bottleneck for the performance of machine learning models.