Chest X-Ray Based AI-Driven Method for Detection of COVID-19 Infected Patients

The global health and economy were devastated by the COVID-19 pandemic. The limited availability of COVID-19 test kits is to blame for this unavoidable diagnostic lag. Therefore, there is an immediate need to develop new low-cost and lowresource diagnostic methods. Chest X-ray is the first step in making a correct diagnosis of COVID-19 since it may easily detect any abnormalities in the chest (e.g., lung inflammation). Additionally, most hospitals have X-ray machines that can be used for early diagnosis of COVID-19. Although early COVID-19 diagnosis is crucial to effective treatment, a lack of radiologists is a major limiting issue. This research provides a method based on artificial intelligence, medical expertise, and deep Convolutional Neural Networks for the early diagnosis of COVID-19 from chest X-ray pictures (CNNs). Specifically, we aim for optimal performance in detecting COVID-19 by building and tuning a deep learning model. The suggested method achieves above 99 per cent accuracy in recognizing COVID-19, as shown by experimental findings on recent benchmark datasets.