ABSTRACT
A new coronavirus that caused the Covid-19 sickness, has already elevated the threat to humans. The virus is quickly spreading around the planet. Therefore, in order to detect sick individuals and stop the infection from spreading, it is vital that we develop fast diagnostic tests. The advancement of machine learning would make it possible to implement pre- ventative actions as soon as possible by enabling early detection of Covid19. However, insufficient sample sizes, particularly chestX-ray pictures, has made it more challenging to diagnose this ailment. In this study, we examined a number of these recently created transfer learning-based CNN models that can identify COVID-19 in lung CT or images of X-ray to diagnose Covid-19 using images of X-ray. We gathered data on the research resources that are readily available. We looked into and examined datasets, pre-processing methods, segmentation approaches, extraction of features, classification, and experimentation outcomes that could be useful for determining future research paths in the area of applying transfer learning based CNN models to diagnose COVID-19 disease. We have analyzed various models such as ResNet50, DenseNet-21, VGG-16, ImageNet, and some hybrid models and evaluated their performance matrix with a particular set of data used in their research work. Additionally, in orderfor a model to perform at its best, it is observed that there aren't enough data sets of COVID-19-infected individuals. This calls for augmentation, segmentation, and domain adaptation in transfer learning. © 2022 IEEE.
ABSTRACT
Objective: To determine the barriers to the maintenance of COVID 19 cross infection control protocols among medical and dental practitioners Methodology: A cross sectional study was conducted in College of Dentistry, Sharif Medical and Dental College, Lahore from July 2021 to July 2022 on medical and dental practitioners. The sampling technique employed was convenient sampling. Medical and dental practitioners irrespective of their age, gender and specialty of practice were included in the study. Data was collected using a pre-validated questionnaire with a Cronbach alpha value of 0.7. Result(s): There was s statistically significant difference in the scores of barriers to maintenance of COVID 19 cross infection control protocols of overcrowding in the hospital (p= <=0.001), limitation of infection control material (p=<=0.001), insufficient training in infection control (p=0.05), lack of handwashing (p=0.022), not wearing a mask while examining the patient (p=<=0.001) and lack of knowledge about mode of transmission of COVID 19 (P=0.036) Conclusion(s): The barriers faced to maintenance of cross infection control protocols pertaining to the hospital administration were reported to be higher for medical practitioners in comparison to the dental practitioners. The barriers faced to maintenance of cross infection control protocols pertaining to the attitude and practices of health care workers were also higher for medical practitioners in comparison to the dental practitioners. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.
ABSTRACT
OBJECTIVE: The aim of this study was to assess the barriers to online learning faced by undergraduate dental students. METHODOLOGY: A descriptive cross-sectional study was conducted on students of Bachelor of Dental Surgery from College of Dentistry, Sharif Medical and Dental College Lahore. A pre-validated questionnaire with a Cronbach alpha 0.94 was used. RESULTS: All the factor scores were found to be statistically significantly different across all four years of Bachelor of Dental Surgery, instructor, and personal problems (p0.01), motivational and time interruption (p=0.003), lack of support services (p=0.002), lack of pre-requisite skills (p=0.006), technical problems (p=0.004) and lack of social interactions (p=0.008). CONCLUSION: The association was strong between year of study and motivational problems and time interruptions, lack of support services, lack of pre-requisite skills. The association between year of study and instructor and personal problems as well as technical problems was medium. The highest mean rank score for the barrier of instructor and personal problems, motivational problems and time interruptions, lack of support services was highest for final year students and the least for second year. The highest mean rank score for technical problems and lack of social interaction was of first year and the least for second year students.