An Optimized Deep Learning Model for Automatic Diagnosis of COVID-19 Using Chest X-Ray Images

COVID-19 has caused havoc throughout the world in the last two years by infecting over 455 million people. Development of automatic diagnosis software tools for rapid screening of COVID-19 via clinical imaging such as X-ray is vital to combat this pandemic. An optimized deep learning model is designed in this paper to perform automatic diagnosis on the chest X-ray (CXR) images of patients and classify them into normal, pneumonia and COVID-19 cases. A convolutional neural network (CNN) is employed in optimized deep learning model given its excellent performances in feature extraction and classification. A particle swarm optimization with multiple chaotic initialization scheme (PSOMCIS) is also designed to fine tune the hyperparameters of CNN, ensuring the proper training of network. The proposed deep learning model, namely PSOMCIS-CNN, is evaluated using a public database consists of the CXR images with normal, pneumonia and COVID-19 cases. The proposed PSOMCIS-CNN is revealed to have promising performances for automatic diagnosis of COVID-19 cases by producing the accuracy, sensitivity, specificity, precision and F1 score values of 97.78%, 97.77%, 98.8%, 97.77% and 97.77%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The mediating effect of psychological strain between person-job fit and employees' wellbeing

Unlike the common stress, higher level of strains is a more serious issue since it can strongly affect one's psychological state and behavioural actions. Among these stressors include the workplace stressors which were constantly associated with job dissatisfaction, mental stress and cardiovascular health. The P-E fit theory alluded that the mismatch between the individual and the environment may result in psychological strains, consequently affecting the individual's wellbeing physically and mentally. Hence, psychological strain is regarded as an influential factor on the state of employees' wellbeing. This study aims to investigate the mediating effect of psychological strain on the relationship between Person-Job Fit (PJF) and employees' wellbeing (WB). This cross-sectional study was conducted among employees of participating banks in Kuala Lumpur, Malaysia. Self-reported questionnaires were distributed via an online survey in the first few months of 2020 as part of data collection. The SEM analysis involving the measurement model, structural model and mediation analysis was conducted through PLS-SEM. The finding reveals that the psychological strain has a full, indirect mediating effect on the relationship between the PJF and WB, implying that it is an appropriate mechanism in explaining this relationship. This study sets a new direction for future studies by highlighting the role of psychological strain in predicting health outcomes besides providing the theoretical groundwork for occupational stress research using the P-E fit theory. © 2020 Ubiquity Press. All rights reserved.