The knowledge and perception about COVID-19 among medical imaging professionals

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.

BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification

Automatic medical image analysis (e.g., medical image classification) is widely used in the early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable accurate disease detection and treatment. Nowadays, deep learning (DL)-based CAD systems have been able to achieve promising results in most of the healthcare applications. Also, uncertainty quantification in the existing DL methods have not gained enough attention in the field of medical research. To fill this gap, we propose a novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF). To deal with uncertainty, we applied the Monte Carlo (MC) dropout during inference to obtain the mean and standard deviation of the predictions. The proposed model has two main strategies: direct and cross validated using four different medical image datasets. Our experimental results demonstrate that the proposed model is efficient for medical image classification in real clinical settings. (c) 2021 Elsevier Inc. All rights reserved.