Lightweight Method for the Rapid Diagnosis of Coronavirus Disease 2019 from Chest X-ray Images using Deep Learning Technique

A rapid screening method is required for screening coronavirus disease 2019 (COVID-19) patients. Therefore, we proposed a model based on DenseNet-201 to detect and differentiate COVID-19 patients from normal people and patients with other bacterial/viral cases of pneumonia using chest X-ray images. Our four-class model was found to have an accuracy of 91.01 ± 1.86 (mean ± standard deviation) and a sensitivity of 92.65 ± 1.28 using a five-fold cross-validation method. Moreover, it was a relatively lightweight and robust model with a simplified structure and fewer parameters, training, and testing epochs. As a supplementary diagnosis tool, physicians can detect COVID-19 faster using this model. © 2021 IEEE.

Covidense: Providing a suitable solution for diagnosing Covid-19 lung infection based on deep learning from chest X-ray images of patients

Purpose: Coronavirus disease 2019 (Covid-19), first reported in December 2019 in Wuhan, China, has become a pandemic. Chest imaging is used for the diagnosis of Covid-19 patients and can address problems concerning Reverse Transcription-Polymerase Chain Reaction (RT-PCR) shortcomings. Chest X-ray images can act as an appropriate alternative to Computed Tomography (CT) for diagnosing Covid-19. The purpose of this study is to use a Deep Learning method for diagnosing Covid-19 cases using chest X-ray images. Thus, we propose Covidense based on the pre-trained Densenet-201 model and is trained on a dataset comprising chest X-ray images of Covid-19, normal, bacterial pneumonia, and viral pneumonia cases. Materials and Methods: In this study, a total number of 1280 chest X-ray images of Covid-19, normal, bacterial and viral pneumonia cases were collected from open access repositories. Covidense, a convolutional neural network model, is based on the pre-trained DenseNet-201 architecture, and after pre-processing the images, it has been trained and tested on the images using the 5-fold cross-validation method. Results: The accuracy of different classifications including classification of two classes (Covid-19, normal), three classes 1 (Covid-19, normal and bacterial pneumonia), three classes 2 (Covid-19, normal and viral pneumonia), and four classes (Covid-19, normal, bacterial pneumonia and viral pneumonia) are 99.46%, 92.86%, 93.91 %, and 91.01% respectively. Conclusion: This model can differentiate pneumonia caused by Covid-19 from other types of pneumonia, including bacterial and viral. The proposed model offers high accuracy and can be of great help for effective screening. Thus, reducing the rate of infection spread. Also, it can act as a complementary tool for the detection and diagnosis of Covid-19. Copyright © 2021 Tehran University of Medical Sciences.

The application of ICT techs (mobile-assisted language learning, gamification, and virtual reality) in teaching english for secondary school students in malaysia during covid-19 pandemic

With the sudden changes caused by the ongoing worldwide pandemic in Malaysia and rapid progress in the educational learning system towards online learning, some teachers wondered about the application of technology in students' education and how it would impact their learning process. Therefore, the main objectives of this research are (1) to contextually understand the university interns' perception of ICT Techs (MALL, Gamification, and VR) in teaching English for secondary school students during the Covid-19 Pandemic in Malaysia, and (2) to determine which of these ICT Techs (MALL, Gamification, and VR) would be most preferred by the interns for teaching English to secondary school students in Malaysia. The research design for this study was quantitative and a web-based questionnaire was adapted from three articles (Mihaela Badea, 2015;Huseyin Oz, 2015;and Ali Rahimi, Niloofar Seyed Golshan & Hooman Mohebi, 2013). The reliability test indicated a value of 0.866 with Cronbach's Alpha reliability statistic. From a total of 63 university interns selected from a private university in Malaysia, the results indicated that 38.1% chose MALL, 33% chose Gamification, and 29% chose VR, as their preferred technology to teach English for secondary school students during this Covid-19 Pandemic in Malaysia. Evidence suggested that online learning can be more effective for students where they can control their own learning pace, compared to learning in a classroom environment. Thus, future English teachers should explore and apply innovative pedagogical methods in the teaching-learning process during pandemic outbreak in Malaysia, while contributing to the development of motivation, participation, and engagement among secondary school students in acquiring the English language. © 2020 by authors.