Implementing Data Augmentation Techniques Using Conditional Generative Adversarial Network-Based upon Chest X-Ray Images
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66336-9_38
Reference11 articles.
1. Acar, E., Şahin, E., Yılmaz, İ: Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images. Neural Comput. Applic. 33, 17589–17609 (2021). https://doi.org/10.1007/s00521-021-06344-5
2. Goel, T., Murugan, R., Mirjalili, S., et al.: Automatic screening of COVID-19 using an optimized generative adversarial network. Cogn. Comput. (2021). https://doi.org/10.1007/s12559-020-09785-7
3. Data augmentation using Generative Adversarial Networks (GANs) for GAN-based detection of Pneumonia and COVID-19 in chest X-ray images. ScienceDirect (2021). https://doi.org/10.1016/j.imu.2021.100779
4. Al-Shargabi, A.A., Alshobaili, J.F., Alabdulatif, A., Alrobah, N.: COVID-CGAN: efficient deep learning approach for COVID-19 detection based on CXR images using conditional GANs. MDPI (2021). https://doi.org/10.3390/app11167174
5. Mehta, T., Mehendale, N.: Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models. Res. Biomed. Eng. 37, 803–813 (2021). https://doi.org/10.1007/s42600-021-00174-z
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