Abstract: The occurrence of coronavirus (COVID-19), which causes respiratory illnesses, is higher than in 2003. (SARS). COVID-19 and SARS are both spreading over regions and infecting living beings, with more than 73,435 deaths and more than 2000 deaths documented as of August 12, 2020. In contrast, SARS killed 774 lives in 2003, whereas COVID-19 claimed more in the shortest amount of time. However, the fundamental difference between them is that, after 17 years of SARS, a powerful new tool has developed that could be utilized to combat the virus and keep it within reasonable boundaries. One of these tools is machine learning (ML). Recently, machine learning (ML) has caused a paradigm shift in the healthcare industry, and its use in the COVID-19 outbreak could be profitable, especially in forecasting the location of the next outbreak. The use of AI in COVID-19 diagnosis and monitoring can be accelerated, reducing the time and cost of these processes. As a result, this study uses ANN and CNN techniques to detect COVID-19 from chest x-ray pictures, with 95% and 75% accuracy, respectively. Machine learning has greatly enhanced monitoring, diagnosis, monitoring, analysis, forecasting, touch tracking, and medication/vaccine production processes for the Covid-19 disease outbreak, reducing human involvement in nursing treatment.

Micheal Olaolu Arowolo, Marion Olubunmi Adebiyi, Eniola Precious Michael, Happiness Eric Aigbogun, Sulaiman Olaniyi Abdulsalam and Ayodele Ariyo Adebiyi, “Detection of COVID-19 from Chest X-Ray Images using CNN and ANN Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130689

@article{Arowolo2022,
title = {Detection of COVID-19 from Chest X-Ray Images using CNN and ANN Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130689},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130689},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Micheal Olaolu Arowolo and Marion Olubunmi Adebiyi and Eniola Precious Michael and Happiness Eric Aigbogun and Sulaiman Olaniyi Abdulsalam and Ayodele Ariyo Adebiyi}
}