ABSTRACT
The earlier detection and accurate diagnosis of COVID seem to be a global problem. It is difficult to make a large number of testing equipment, but then again, their reliability is relatively poor. Recent research indicates the usefulness of chest x-ray pictures in identifying COVID. This study presents a deep learning algorithm developed from the ground up to categorize as well as confirm the existence of COVID in a set of X-ray imaging data. We designed a CNN architecture from the ground up to retrieve elements from provided X-ray data to categorize them and identify the individual contaminated with COVID. Our strategy may aid in mitigating the consistency issues while working with medical data. In contrast to some other classifying activities with a large enough image database, obtaining large X-ray datasets for this classification job is challenging. So, we applied multiple data enhancement techniques to maximize the accurateness, achieving a significant accuracy of 97.75 percent. © 2022 IEEE.
ABSTRACT
SARS-CoV-2 (n-coronavirus) is a global pandemic that causes the deaths of millions of people worldwide. It can cause Pneumonia and severe acute respiratory syndrome (SARS) and lead to death in severe cases. It is an asymptomatic disease that hardens our life and work conditions. As there is no effective treatment available, many scientists and researchers are trying their best to fight the pandemic. This paper focused on the coronavirus pandemic situation in the global and Bangladesh region and its related effects and future status. We have utilized different information representation and machine learning calculations to recreate the affirmed, recuperated, and passing cases. We believe the research will help scientists, researchers, and ordinary people predict and analyze this pandemic's impact. Finally, the comparison and analysis of different models and algorithms successfully showed our visualization and prediction success.