Preliminary Stages for COVID-19 Detection Using Image Processing

Abstract

COVID-19 was first discovered in December 2019 in Wuhan. There have been reports of thousands of illnesses and hundreds of deaths in almost every region of the world. Medical images, when combined with cutting-edge technology such as artificial intelligence, have the potential to improve the efficiency of the public health system and deliver faster and more reliable findings in the detection of COVID-19. The process of developing the COVID-19 diagnostic system begins with image accusation and proceeds via preprocessing, feature extraction, and classification. According to literature review, several attempts to develop taxonomies for COVID-19 detection using image processing methods have been introduced. However, most of these adhere to a standard category that exclusively considers classification methods. Therefore, in this study a new taxonomy for the early stages of COVID-19 detection is proposed. It attempts to offer a full grasp of image processing in COVID-19 while considering all phases required prior to classification. The survey concludes with a discussion of outstanding concerns and future directions.

COVID-19 lateral flow test image classification using deep CNN and StyleGAN2

“…Many other studies including Sahlol et al ( 2020 ), Thuseethan et al ( 2021 ), Alhaj et al ( 2022 ), and Aggarwal et al ( 2022 ) attempted to detect the presence of COVID-19 infection in chest CT and X-ray images. However, the drawbacks of using these imaging techniques are unnecessary exposure to radiation, lack of CT and X-ray machines in resource-poor regions, the need for highly skilled medics for interpretation, and unnecessary travel which increases the risk of transmission and exposure.…”