COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images.

The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus. In the literature, various models based on machine learning (ML) and deep learning (DL) are introduced to detect many pneumonias using chest X-ray images. The cornerstone in this paper is the use of pretrained deep learning CNN architectures to construct an automated system for COVID-19 detection and diagnosis. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pre-trained CNN models, AlexNet and Xception. Hence, we propose COVID-AleXception: a neural network that is a concatenation of the AlexNet and Xception models for the overall improvement of the prediction capability of this pandemic. To evaluate the proposed model and build a dataset of large-scale X-ray images, there was a careful selection of multiple X-ray images from several sources. The COVID-AleXception model can achieve a classification accuracy of 98.68%, which shows the superiority of the proposed model over AlexNet and Xception that achieved a classification accuracy of 94.86% and 95.63%, respectively. The performance results of this proposed model demonstrate its pertinence to help radiologists diagnose COVID-19 more quickly.

The Challenges Faced by Undergraduate Women during the COVID-19 Pandemic in Saudi Arabia

The latest COVID-19 pandemic is a specific and unusual event. It forced universities to close their doors and move fully to distance education. The sudden shift from traditional education to full distance education created many challenges and difficulties for universities, faculty members, and students. This study aims to investigate the challenges and obstacles faced by undergraduate women in Saudi Arabia universities while using online-only learning during the COVID-19 pandemic outbreak. Moreover, this study provides some recommendations to address these challenges from undergraduate women’s perspectives. The study used a qualitative research methodology to investigate the challenges and difficulties. The participants were undergraduate women selected using random purposive sampling technique from the population of College of Computer and Information Sciences (CCIS) at Princess Nourah Bint Abdulrahman University (PNU), Riyadh, Saudi Arabia. The final sample consisted of 68 undergraduate women who responded to a predesigned open-ended questionnaire that was sent via e-mail to targeted respondents. The data gathered from the questionnaire were analyzed using qualitative content analysis. Results of the research revealed that the most obvious challenges identified by the participants were technical issues, lack of in-person interaction, distractions and time management, lack of a systematic schedule, stress and psychological pressure, missing the traditional university environment, limited availability of digital devices, and lack of access to external learning resources.