Covid-19 detection and overcome the scarcity of chest X-Ray datasets based on transfer learning and GAN model

Diseases which affect the respiratory system are considered some of the most dangerous, since defects in the breathing process may lead to death. Currently, the coronavirus is one of the most complex strains of the corona family of diseases for which, as yet, there is no successful vaccine. Therefore, detecting the virus is of the utmost importance to global health. The use of deep learning techniques is considered to be a successful method by which to diagnose such diseases. However, there is a lack of data which models deep learning techniques required for training. In this paper, we suggest a method for data augmentation based on the CGAN model. To synthesize realistic chest X-ray images, we proposed to set edge information of the image to the generator, which is used as supporting information to increase the reality of the generated images. A MobileNet CNN model was used for diagnosing COVID-19. When this suggestion was applied to chest data, the diagnostic results were very satisfactory which the accuracy exceeds 99% in some cases. © 2021 IEEE.

Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

The recent Coronavirus Infectious Disease 2019 (COVID-19) pandemic has caused an unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. Depression can cause serious emotional, behavioral, and physical health problems with significant consequences, both personal and social costs included. This article studies community depression dynamics due to the COVID-19 pandemic through user-generated content on Twitter. A new approach based on multimodal features from tweets and term frequency-inverse document frequency (TF-IDF) is proposed to build depression classification models. Multimodal features capture depression cues from emotion, topic, and domain-specific perspectives. We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia. Our novel classification model is capable of extracting depression polarities that may be affected by COVID-19 and related events during the COVID-19 period. The results found that people became more depressed after the outbreak of COVID-19. The measures implemented by the government, such as the state lockdown, also increased depression levels. IEEE