A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

Spreading of Coronavirus Globally and Pandemic Crisis

Objectives: There is a lot of speculation, debate, and hypothesis about the new coronavirus disease 2019 (COVID-19) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also the world famous media British Broadcasting Corporation (BBC), National Center for Biotechnology Information (NCBI), and World Health Organization (WHO) are informing their emergency concern for the public attention. But we are far behind yet to say the origin of COVID-19 and its outbreak. The main purpose of this study is to put an end to all speculations, fantasies, theories and debates.

Spreading of Coronavirus Globally and Pandemic Crisis

ABSTRACT Objectives: There is a lot of speculation, debate, and hypothesis about the new coronavirus disease 2019 (COVID-19) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also the world famous media British Broadcasting Corporation (BBC), National Center for Biotechnology Information (NCBI), and World Health Organization (WHO) are informing their emergency concern for the public attention. But we are far behind yet to say the origin of COVID-19 and its outbreak. The main purpose of this study is to put an end to all speculations, fantasies, theories and debates.

Public Health in Asia during the COVID-19 Pandemic

Every nation in Asia has dealt with COVID-19 differently and with varying levels of success in the absence of clear and effective leadership from the WHO. As a result, the WHO’s role in Asia as a global health organization is coming under increasing pressure. As its credibility is slowly being eroded by public displays of incompetence and negligence, it has also become an arena of contestation. Moreover, while the pandemic continues to undermine the future of global health governance as a whole, the highly interdependent economies in Asia have exposed the speed with which pandemics can spread, as intensive regional travel and business connections have caused every area in the region to be hit hard. The migrant labor necessary to sustain globalized economies has been strained and the security of international workers is now more precarious than ever, as millions have been left stranded, seen their entry blocked, or have limited access to health services. This volume provides an accessible framework for the understanding the effects of the COVID-19 pandemic in Asia, with a specific emphasis on global governance in health and labor.

Face Mask Detection Using Deep Learning and Computer Vision

COVID-19 pandemic has rapidly affected our day-to-day life the world trade and movements. Wearing a face mask is very essentials for protecting against virus. People also wear mask to cover themselves in order to reduce the spread of covid virus. The corona virus covid-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask in public area according to the world health organization (WHO). The covid-19 pandemic forced government across the world to impose lockdowns to prevent virus transmission report indicates that wearing face mask while at work clearly reduce the risk of transmission .we will use the dataset to build a covid-19 face mask detector with computer vision using python,opencv,tensorflow,keras library and deep learning. Our goal is to identify whether the person on image or live video stream is wearing mask or not wearing face mask this can help to society and whole organization to avoid the transfer of virus one person to antother.we used computer vision and deep learning modules to detect a with mask image and without mask image. Keywords: face detection, face recognition, CNN, SVM, opencv, python, tensorflow, keras.

An Effect of Covid Treatment on Oral Cavity

Covid-19 means Corona Virus Disease which is an emergency disease declared by World Health Organization. Its first case was reported on December, 2019 in a city of China name Wuhan. Responsible virus for Covid-19 is SARS-CoV-2. Disease can be transmitted by Sneezing, Coughing, Close Contact etc. Patient of Covid-19 advise to isolate themselves for minimum 14 days either in Home or Hospital setup.

Automatic Speech Classifier for Mild Cognitive Impairment and Early Dementia

The World Health Organization estimates that 50 million people are currently living with dementia worldwide and this figure will almost triple by 2050. Current pharmacological treatments are only symptomatic, and drugs or other therapies are ineffective in slowing down or curing the neurodegenerative process at the basis of dementia. Therefore, early detection of cognitive decline is of the utmost importance to respond significantly and deliver preventive interventions. Recently, the researchers showed that speech alterations might be one of the earliest signs of cognitive defect, observable well in advance before other cognitive deficits become manifest. In this article, we propose a full automated method able to classify the audio file of the subjects according to the progress level of the pathology. In particular, we trained a specific type of artificial neural network, called autoencoder, using the visual representation of the audio signal of the subjects, that is, the spectrogram. Moreover, we used a data augmentation approach to overcome the problem of the large amount of annotated data usually required during the training phase, which represents one of the most major obstacles in deep learning. We evaluated the proposed method using a dataset of 288 audio files from 96 subjects: 48 healthy controls and 48 cognitively impaired participants. The proposed method obtained good classification results compared to the state-of-the-art neuropsychological screening tests and, with an accuracy of 90.57%, outperformed the methods based on manual transcription and annotation of speech.

Development of a World Health Organization Programme for School Mental Health in the Eastern Mediterranean Region

Background: Schools provide an exceptional opportunity for mental health promotion and intervention. Aims: To describe the development of a World Health Organization (WHO) School Mental Health Program (SMHP) in the Eastern Mediterranean Region. Methods: Two tenets guided development of the SMHP: (1) it used a multitiered system of support framework including 3 tiers of interventions (universal, early and targeted); and (2) interventions must be feasible for implementation by non-mental health professionals. Results: The WHO SMHP is organized into a background section, followed by 3 modules: Social–Emotional Childhood Development; Mental Health Promoting Schools (Promotion and Prevention); and Addressing Student Mental Health Problems in Your Classroom, including specific classroom strategies and case examples. Conclusion: Developing an appropriate curriculum sensitive to the needs of individual countries requires involvement of those familiar with schooling in those countries, with mental health priorities and practices that promote mental health, and to coalesce school staff, parents and community members in the service of their children.