Smart Artificial Intelligence techniques using embedded band for diagnosis and combating COVID-19.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Epidemiology

Background:

• The SARS-CoV-2 virus caused a global COVID-19 pandemic starting in December 2019.
• The World Health Organization declared COVID-19 a worldwide epidemic on March 11, 2020.
• Lung infections are a primary concern in COVID-19 patients.

Purpose of the Study:

• To detect the spread of the COVID-19 virus.
• To rapidly address lung infections caused by the virus.
• To explore Artificial Intelligence (AI) as a tool against the COVID-19 epidemic.

Main Methods:

• Utilized AI and computational techniques for COVID-19 analysis.
• Employed Deep Learning methods, specifically Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN).
• Applied RNN and CNN to classify and identify regions affected by COVID-19 in lung images.

Main Results:

• AI techniques, including RNN and CNN, demonstrated potential in diagnosing COVID-19 infections.
• The study highlights AI's role in medical imaging for diagnosis and lung delineation.
• AI applications extend to lesion measurement, disease tracking, and patient outcome prediction.

Conclusions:

• AI, particularly Deep Learning models like RNN and CNN, offers a rapid diagnostic approach for COVID-19.
• AI applications are vital for various aspects of managing the COVID-19 pandemic, from diagnosis to drug development and epidemiology.