ABSTRACT
The novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide, and it has become a greater risk for human beings. To curb the community transmission of this virus, rapid detection and identification of the affected people via a quick diagnostic process are necessary. Media studies have shown that most COVID-19 victims endure lung disease. For rapid identification of the affected patient, chest CT scans and X-ray images have been reported to be suitable techniques. However, chest X-ray (CXR) shows more convenience than the CT imaging techniques because it has faster imaging times than CT and is also simple and cost-effective. Literature shows that transfer learning is one of the most successful techniques to analyze chest X-ray images and correctly identify various types of pneumonia. Since SVM has a remarkable aspect that tremendously provides good results using a small data set thus in this study we have used SVM machine learning algorithm to diagnose COVID-19 from chest X-ray images. The image processing tool called RGB and SqueezeNet models were used to get more images to diagnose the available data set. Our adopted model shows an accuracy of 98.8% to detect the COVID-19 affected patient from CXR images. It is expected that our proposed computer-aided detection tool (CAT) will play a key role in reducing the spread of infectious diseases in society through a faster patient screening process.
ABSTRACT
The goal of the prospective research study is to highlight the radiology undergraduate student's perspective on the quality of education during Pandemic, to validate the pros and cons of online learning based on student's perception. An assorted cross-sectional study was designed, and a deductive approach was applied using a semi-structured questionnaire from the students of the Radiology Technology Department at Taibah University from September 2020 to July 2021. A total of 132 radiology students of all levels responded to the questionnaire including 70 females and 32 males. The data obtained from the survey was collected and analyzed using a social sciences-related statistics package (IBM SPSS). A chi-square test has been used to find the association (P-value). The results showed a significant association between satisfaction with the quality of the blackboard website and having no problems with attending online classes and exams (P <0.001 and P =0.003). The findings also disclosed that technology increased student engagement with class discussion believed that the overall quality of online education was excellent. No statistical difference can be found between the academic years and the overall quality of online education (p-value=0.187). These outcomes may be used to enhance learning quality, pros, and cons of e-learning, and online education quality from students' perspectives, and the impacts on them. Furthermore, in a post-pandemic world, these discoveries are critical for the future of education.