Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases

Abstract

Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods.

Readiness Assessment of Public University in Bangladesh for Education 4.0: A Case Study of Shahjalal University of Science and Technology – Sylhet, Bangladesh

“…The nuances of these pre-trained models have also been explored. Brown and Patel (2022) probed the efficacy of partially freezing model weights for COVID-19 detection, emphasizing the potential dual benefits: leveraging the pre-trained model's existing knowledge while fine-tuning the specificity of the COVID-19 dataset[9]. Such methodologies mitigate challenges such as underfitting and overfitting, crucial for achieving diagnostic accuracy.…”