Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.

Area of Science:

• Medical diagnosis and predictive analytics
• Artificial intelligence in healthcare

Background:

• The COVID-19 pandemic necessitates advanced diagnostic tools due to rapid mutation and global impact.
• Existing diagnostic methods require enhancement for accurate and timely disease prediction.

Purpose of the Study:

• To present key point analytics from medical images for COVID-19 diagnosis and prediction using Generative Adversarial Networks (GANs).
• To enhance the performance of predictive analytics in medical imaging.

Main Methods:

• Utilized Generative Adversarial Networks (GANs), a high-performance approach employing advanced neural networks.
• Applied deep learning models for the generation, transformation, and presentation of key points from COVID-19 medical image datasets (X-Ray, CT Scan).
• Integrated GANs with classical neural networks for improved predictive analytics.

Main Results:

• Demonstrated the capability of GANs to analyze patterns in medical images for diagnostic insights.
• Achieved enhanced predictive analytics performance compared to traditional multi-layer neural networks.
• Facilitated effective predictive mining and knowledge discovery on benchmark datasets.

Conclusions:

• GANs offer a powerful approach for improving the accuracy and performance of medical image analysis in disease prediction.
• The integration of GANs in diagnostic workflows can lead to more effective knowledge discovery and patient management for conditions like COVID-19.