Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review

Area of Science:

• Medical Imaging
• Computer Vision
• Artificial Intelligence

Background:

• The COVID-19 pandemic necessitates rapid advancements in healthcare solutions.
• Existing healthcare infrastructures face limitations during widespread outbreaks.
• Innovative automation technologies present viable strategies against the virus.

Purpose of the Study:

• To review image acquisition, segmentation, diagnosis, avoidance, and management techniques for COVID-19.
• To provide an analytical comparison of algorithms proposed by researchers.
• To highlight future research challenges and motivations in combating coronavirus.

Main Methods:

• Review of various image processing techniques applied to COVID-19 detection and management.
• Analytical comparison of different algorithms used in coronavirus research.
• Discussion on the clinical impact of computer vision and deep learning.

Main Results:

• Computer vision, machine learning, deep learning, and AI show significant potential in addressing COVID-19.
• Medical imaging techniques like CT and X-ray are crucial for diagnosis.
• A comprehensive overview of current research and proposed algorithms is presented.

Conclusions:

• Computer vision and deep learning offer encouraging solutions for COVID-19 detection and management.
• Further research is motivated by identified challenges in image acquisition, segmentation, and diagnosis.
• Dermatologists can gain a better understanding of these technologies for clinical application.