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      An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19

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          Abstract

          Nowadays, COVID-19 is spreading at a rapid rate in almost all the continents of the world. It has already affected many people who are further spreading it day by day. Hence, it is the most essential to alert nearby people to be aware of it due to its communicable behavior. Till May 2020, no vaccine is available for the treatment of this COVID-19, but the existing technologies can be used to minimize its effect. Cloud/fog computing could be used to monitor and control this rapidly spreading infection in a cost-effective and time-saving manner. To strengthen COVID-19 patient prediction, Artificial Intelligence(AI) can be integrated with cloud/fog computing for practical solutions. In this paper, fog assisted the internet of things based quality of service framework is presented to prevent and protect from COVID-19. It provides real-time processing of users’ health data to predict the COVID-19 infection by observing their symptoms and immediately generates an emergency alert, medical reports, and significant precautions to the user, their guardian as well as doctors/experts. It collects sensitive information from the hospitals/quarantine shelters through the patient IoT devices for taking necessary actions/decisions. Further, it generates an alert message to the government health agencies for controlling the outbreak of chronic illness and for tanking quick and timely actions.

          Highlights

          • •

            In this paper, Fog Assisted Internet of Things Based Quality of Service Framework is presented to Prevent and Protect from COVID-19.

          • •

            It provides real-time processing of users' health data to predict the COVID- 19 infection by observing their symptoms and immediately generates an emergency alert, medical reports, and significant precautions to the user, their guardian as well as doctors/experts.

          • •

            It collects sensitive information from the hospitals/quarantine shelters through the patient IoT devices for taking necessary actions/decisions.

          • •

            Further, it generates an alert message to the government health agencies for controlling the outbreak of chronic illness and for tanking quick and timely actions.

          • •

            Moreover, the information of the infected individual, along with his/her live location, is also shared with the nearest healthcare/government agencies through the Google map.

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          Deep Learning COVID-19 Features on CXR using Limited Training Data Sets

          Yujin Oh, Sangjoon Park, Jong Ye (2020)
          Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
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            A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT

            Xinggang Wang, Xianbo Deng, Qing Fu … (2020)
            Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious; the COVID-19 lesions are localized by combining the activation regions in the classification network and the unsupervised connected components. 499 CT volumes were used for training and 131 CT volumes were used for testing. Our algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training. The easily-trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.
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              CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection

              Abdul A. Waheed, Muskan Goyal, Deepak Gupta … (2020)
              Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.
              Article 0 comments Cited 198 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Glob Transit
                Journal ID (iso-abbrev): Glob Transit
                Title: Global Transitions
                Publisher: The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.
                ISSN (Electronic): 2589-7918
                Publication date PMC-release: 12 November 2020
                Publication date (Electronic): 12 November 2020
                Affiliations
                [a ]Department of Computer Science & Engineering, Punjabi University, Patiala, IN, India
                [b ]Department of Electronics & Communication Engineering, Punjabi University, Patiala, IN, India
                Author notes
                [∗ ]Corresponding author.
                Article
                Publisher Item ID: S2589-7918(20)30024-4
                DOI: 10.1016/j.glt.2020.11.002
                PMC ID: 7659515
                PubMed ID: 33205037
                SO-VID: 4bfe5b1c-a919-4f89-a47b-6e552e3816f9
                Copyright © © 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 4 July 2020
                Date revision received : 9 October 2020
                Date accepted : 1 November 2020
                Categories
                Subject: Article

                Keywords: smart city,quality of service framework,ensemble model,cloud/fog computing,artificial intelligence,covid 19
                Data availability:
                Keywords: smart city, quality of service framework, ensemble model, cloud/fog computing, artificial intelligence, covid 19

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