Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review

Area of Science:

• Computer Science
• Medical Imaging
• Artificial Intelligence

Background:

• COVID-19 is a contagious respiratory illness caused by a coronavirus.
• Vulnerable populations include the elderly and those with underlying health conditions.
• No definitive treatments or vaccines are currently available, though clinical trials are ongoing.

Purpose of the Study:

• To explore Machine Learning (ML) and Deep Learning (DL) techniques for COVID-19 detection.
• To address challenges in COVID-19 data analysis, such as limited datasets and expert annotations.
• To investigate the application of AI in adapting existing models for novel coronavirus challenges.

Main Methods:

• Review of Machine Learning techniques applied to analyze Corona Virus Data.
• Exploration of Deep Learning approaches for disease detection using medical images (X-rays, CT scans).
• Discussion of AI tool development for adapting models to new infectious diseases.

Main Results:

• ML and DL show promise in detecting COVID-19 from medical images.
• AI can be rapidly developed and adapted to address emerging infectious disease challenges.
• Overcoming data scarcity and annotation limitations is crucial for AI model development.

Conclusions:

• Machine Learning and Deep Learning are valuable tools for analyzing COVID-19 data.
• AI-driven solutions can aid in the rapid response to new and evolving health threats.
• Further research is needed to overcome data limitations and enhance AI model accuracy for COVID-19 detection.