COVID-19 Patterns Identification using Generative Adversarial Networks Based Implementation
Generative Adversarial Network (GAN)
DOI:
https://doi.org/10.31185/wjcm.Vol1.Iss1.24Keywords:
COVID-19 Data Analytics, Generative Adversarial Network, GAN, Generative Adversarial Network in Medical DiagnosisAbstract
Abstract:
Predictive analytics and medical diagnostics are two of the most important fields of study that have a lot of room for growth. Today, the COVID-19 virus has a huge impact, but it changes a lot. This virus has spread across the world, and there is currently no vaccine for it. The number of cases in India now stands at more than 10,000, and more than 300 people have died from it. Twenty people in the world have COVID. Neuronal network technology has made big changes. The Generative Adversarial Network (GAN) is used to analyse pictures and multimedia data in huge areas with great speed. Medical images from COVID-19 data sets will be looked at to see if they can predict what will happen to patients. Medical images, such as X-rays and CT scans, are used to train the GANs, which build, change, and analyse data sets and key points with advanced deep learning models. If GANs are used in the general prediction study, they can help traditional neural networks outperform them in a lot of places. This study is meant to help people better plan for mining and information exploration by combining work done on Benchmark data sets with more advanced text.
References
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401–4410, 2019.
G. J. Qi, “Loss-sensitive generative adversarial networks on lipschitz densities,” International Journal of Computer Vision, pp. 1–23, 2019.
A. Odena, “Open questions about generative adversarial networks,” Distill, vol. 4, no. 4, pp. 18–18, 2019.
E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. V. Gool, “Generative adversarial networks for extreme learned image compression,” Proceedings of the IEEE International Conference on Computer Vision, pp. 221–231, 2019.
H. Zhang, Z. Zhang, A. Odena, and H. Lee, “Consistency Regularization for Generative Adversarial Networks,” 2019.
N. Caporusso, K. Zhang, G. Carlson, D. Jachetta, D. Patchin, S. Romeiser, . . Walters, and A, “User Discrimination of Content Produced by Generative Adversarial Networks,” in International Conference on Human Interaction and Emerging Technologies, pp. 725–730, Springer, 2019.
J. Hu, M. Li, and P. Gao, “MATGANIP: Learning to Discover the Structure-Property Relationship in Perovskites with Generative Adversarial Networks,” 2019.
C. Wang, C. Xu, X. Yao, and D. Tao, “Evolutionary generative adversarial networks,” IEEE Transactions on Evolutionary Computation, vol. 23, no. 6, pp. 921–934, 2019.
D. Volkhonskiy, I. Nazarov, and E. Burnaev, “Steganographic generative adversarial networks,” Twelfth International Conference on Machine Vision (ICMV 2019), vol. 11433, 2020.
D. Bau, J. Y. Zhu, H. Strobelt, B. Zhou, J. B. Tenenbaum, W. T. Freeman, and A. Torralba, “Visualizing and understanding generative adversarial networks,” 2019.
F. C. Chang, J. J. Chang, C. N. Chou, and E. Y. Chang, “Toward Fusing Domain Knowledge with Generative Adversarial Networks to Improve Supervised Learning for Medical Diagnoses,” 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 77–84, 2019.
A. M. Husein, M. Arsyal, S. Sinaga, and H. Syahputa, “Generative Adversarial Networks Time Series Models to Forecast Medicine Daily Sales
in Hospital,” SinkrOn, vol. 3, no. 2, pp. 112–118, 2019.
Y. Kimura, A. Watanabe, T. Yamada, S. Watanabe, T. Nagaoka, M. Nemoto, ... & K. Ishii. “AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia”.Annals of Nuclear Medicine, 1-4. 2020
H. Hong, S. Jiang, H. Li, G. Du, Y. Sun, H. Tao, . . Yin, and X, “DeepHiC: A generative adversarial network for enhancing Hi-C data resolution,” PLoS computational biology, vol. 16, no. 2, pp. 1007287–1007287, 2020.
Z. Xie, R. Baikejiang, T. Li, X. Zhang, K. Gong, M. Zhang, . . Qi, and J, “Generative adversarial network based regularized image reconstruction for PET,” Physics in Medicine & Biology, 2020.
A. Oday, N. A. Abu, Z. Z. Abidin, and S. M. Darwish, “A New Descriptor for Smile Classification Based on Cascade Classifier in Unconstrained Scenarios,” Symmetry, vol. 13, no. 5, pp. 805–805, 2021.
D. J. Gagne, H. M. Christensen, A. C. Subramanian, and A. H. Monahan, “Machine learning for stochastic parameterization: Generative adversarial networks in the Lorenz’96 model,” Journal of Advances in Modeling Earth Systems, vol. 12, no. 3, pp. 2019–001896, 2020.
H. Tang, D. Xu, Y. Yan, P. H. Torr, and N. Sebe, “Local class-specific and global image-level generative adversarial networks for semantic-guided scene generation,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7870–7879, 2020.
C. Haarburger, N. Horst, D. Truhn, M. Broeckmann, S. Schrading, C. Kuhl, and D. Merhof, “Multiparametric Magnetic Resonance Image Synthesis using Generative Adversarial Networks,” 2019.
A. Perez, S. Ganguli, S. Ermon, G. Azzari, M. Burke, and D. Lobell, “Semi-supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks (gans) for predicting poverty,” 2019.
D. Mahapatra and B. Bozorgtabar, “Progressive Generative Adversarial Networks for Medical Image Super resolution,” 2019.
B. Peng, X. Huang, S. Wang, and J. Jiang, “A Real-Time Medical Ultrasound Simulator Based on a Generative Adversarial Network Model,” 2019 IEEE International Conference on Image Processing (ICIP), pp. 4629–4633, 2019.
X. Bing, W. Zhang, L. Zheng, and Y. Zhang, “Medical Image Super Resolution Using Improved Generative Adversarial Networks,” IEEE Access, vol. 7, pp. 145030–145038, 2019.
O. Ali, N. A. Abu, Z. Z. Abidin, and S. M. Darwish, “Realistic Smile Expression Recognition Approach Using Ensemble Classifier with Enhanced Bagging,” CMC-COMPUTERS MATERIALS & CONTINUA, vol. 70, no. 2, pp. 2453–2469, 2022.
Z. Yang, Y. Chen, Z. Le, F. Fan, and E. Pan, “Multi-Source Medical Image Fusion Based on Wasserstein Generative Adversarial Networks,” IEEE Access, vol. 7, pp. 175947–175958, 2019.
A. Hassen, S. O. Abter, A. A. Abdulhussein, S. M. Darwish, Y. M. Ibrahim, and W. Sheta, “Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection,” CMC-COMPUTERS MATERIALS & CONTINUA, vol. 68, no. 1, pp. 961–981, 2021.
H. Bae, D. Jung, and S. Yoon, “AnomiGAN: Generative adversarial networks for anonymizing private medical data,” 2019.
F. C. Chang, J. J. Chang, C. N. Chou, and E. Y. Chang, “Toward Fusing Domain Knowledge with Generative Adversarial Networks to Improve Supervised Learning for Medical Diagnoses,” 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 77–84, 2019.
A. Teramoto, T. Tsukamoto, A. Yamada, Y. Kiriyama, K. Imaizumi, K. Saito, and H. Fujita, “Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks,” PloS one, vol. 15, no. 3, pp. 229951–229951, 2020.
D. Sarrut, N. Krah, and J. M. Létang, “Generative adversarial networks (GAN) for compact beam source modeling in Monte Carlo simulations,” Physics in Medicine & Biology, vol. 64, no. 21, pp. 215004–215004, 2019.
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Copyright (c) 2022 Jamal Kh-Madhloom , Maryam Jawad Kadhim , Hussein Najm Abd Ali
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