Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Epidemiology

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, has led to a significant global health crisis with millions of infections and deaths.
• Rapid detection and understanding of disease spread are crucial for effective pandemic management.

Purpose of the Study:

• To present deep learning-based methods for COVID-19 detection using medical imaging (CT and X-ray).
• To perform data analytics on the global spread of COVID-19.
• To systematize current research and provide tools for researchers and practitioners.

Main Methods:

• Application of deep learning algorithms for analyzing chest CT and X-ray images to identify COVID-19.
• Utilizing data analytics techniques to study the worldwide transmission patterns of the virus.

Main Results:

• Demonstrated the efficacy of deep learning in detecting COVID-19 from medical images.
• Provided insights into the global spread dynamics of COVID-19 through data analysis.

Conclusions:

• Deep learning methodologies offer a promising approach for rapid COVID-19 diagnosis.
• Data analytics on disease spread is essential for informed public health strategies during pandemics.