COVID-19 Patterns Identification using Generative Adversarial Networks Based Implementation

Generative Adversarial Network (GAN)

Authors

  • Jamal Kh-Madhloom Computer Sciences and information Technology College, Wasit University, Iraq
  • Maryam Jawad Kadhim Computer Sciences and information Technology College, Wasit University, Iraq
  • Hussein Najm Abd Ali Computer Sciences and information Technology College, Wasit University, Iraq

DOI:

https://doi.org/10.31185/wjcm.Vol1.Iss1.24

Keywords:

COVID-19 Data Analytics, Generative Adversarial Network, GAN, Generative Adversarial Network in Medical Diagnosis

Abstract

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.

 

 

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Published

2022-03-30

Issue

Section

Computer

How to Cite

[1]
J. . Kh-Madhloom, M. . Jawad Kadhim, and H. . Najm Abd Ali, “COVID-19 Patterns Identification using Generative Adversarial Networks Based Implementation: Generative Adversarial Network (GAN)”, WJCMS, vol. 1, no. 1, pp. 10–19, Mar. 2022, doi: 10.31185/wjcm.Vol1.Iss1.24.