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      Application of Deep Learning for Fast Detection of COVID-19 in X-Rays using nCOVnet

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          Abstract

          Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

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          Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

          Lin Li, Lixin Qin, Zeguo Xu … (2020)
          Background Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.
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            Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

            Ioannis Apostolopoulos, Tzani Mpesiana (2020)
            In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.
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              Analysis and forecast of COVID-19 spreading in China, Italy and France

              Duccio Fanelli, Francesco Piazza (2020)
              Highlights • Epidemic spreading • COVID19 • SIR model • Recursive relations and non linear fitting
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                Author and article information

                Contributors
                Harsh Panwar
                P.K. Gupta
                Mohammad Khubeb Siddiqui
                Ruben Morales-Menendez
                Vaishnavi Singh
                Journal
                Journal ID (nlm-ta): Chaos Solitons Fractals
                Journal ID (iso-abbrev): Chaos Solitons Fractals
                Title: Chaos, Solitons, and Fractals
                Publisher: Elsevier Ltd.
                ISSN (Print): 0960-0779
                ISSN (Electronic): 0960-0779
                Publication date PMC-release: 28 May 2020
                Publication date (Electronic): 28 May 2020
                Electronic Location Identifier: 109944
                Affiliations
                [a ]Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, HP, 173 234, India
                [b ]School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, NL, Mexico
                Author notes
                [* ]Corresponding author. khubeb@ 123456tec.mx
                Article
                Publisher ID: S0960-0779(20)30343-X Publisher ID: 109944
                DOI: 10.1016/j.chaos.2020.109944
                PMC ID: 7254021
                PubMed ID: 32536759
                SO-VID: e8cc4765-b2b7-4b5d-9b8b-facde799e4b7
                Copyright © © 2020 Elsevier Ltd. All rights reserved.
                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 : 10 May 2020
                Date accepted : 26 May 2020
                Categories
                Subject: Article

                Keywords: covid-19,detection,x-rays,deep learning,convolutional neural network (cnn),ncovnet
                Data availability:
                Keywords: covid-19, detection, x-rays, deep learning, convolutional neural network (cnn), ncovnet

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