ABSTRACT
The coronavirus invaded the world in late 2019. It includes many subtypes, majorly severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). Jordan has faced enormous hardships in dealing with the abrupt spread of the coronavirus disease of 2019 (COVID-19) pandemic. Jordan has taken severe and deterring measures to combat the disease's spread, such as closing Jordanian schools and institutions. Medical imaging professionals (MIPs) play a vital role in corona patients' diagnosis, management, and treatment planning, and their awareness is essential to understand. This study focuses on medical imaging professionals (MIPs) and their aid in COVID-19 planning. The knowledge and perception of the COVID-19 pandemic were assessed using a live cross-sectional survey conducted during the outbreak. Medical imaging professionals and trainees in private, military, and government hospitals provided data. Regarding the diagnosis of COVID-19, the researchers have found that molecular biology techniques are the first line of defence, whereas nasopharyngeal swabs and the polymerase chain reaction (RT-PCR) are also prevalent among medical professionals for COVID-19 testing. Overall, medical imaging experts and interns in Jordan exhibited expected levels of knowledge and perception. They advised following the CDC and WHO guidelines in their healthcare settings to offer an acceptable approach during the pandemic. [ FROM AUTHOR] Copyright of Health & Social Care in the Community is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Background and Objective: The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems. Methods: Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script's title, "SCovNet" refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets. Results: A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074. Conclusions: The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.