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      CoroNet: A Deep Neural Network for Detection and Diagnosis of COVID-19 from Chest X-ray Images


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          • Classification of Normal, Pneumonia-bacterial, Pneumonia-viral and Covid-19 chest x-ray images.

          • Presented a Deep Convolutional Neural Network Model based on Xception architecture.

          • Used a transfer learning method to initialize model by weight parameters learned on large-scale datasets.

          • Trained the model on a dataset prepared by collecting x-ray images from publically available databases.

          • Achieved an overall accuracy of 89.6% and precision and recall rate for Covid-19 cases are 93% and 98.2%. The results obtained by our proposed model are superior compared to other studies in the literature

          • Promising results indicate that this model can be very helpful to doctors around the world in their fight against Covid-19 Pandemic.


          Background and Objective

          The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays.


          In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases.


          CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available.


          CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

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          Most cited references4

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          Detection of SARS-CoV-2 in different types of clinical specimens

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            Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR

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              Detection of coronavirus disease (covid-19) based on deep features


                Author and article information

                Comput Methods Programs Biomed
                Comput Methods Programs Biomed
                Computer Methods and Programs in Biomedicine
                Elsevier B.V.
                5 June 2020
                5 June 2020
                : 105581
                [1 ]Dept. of Computer Science, Jamia Millia Islamia, New Delhi
                [2 ]Higher Education Department, J&K, India
                [3 ]Lelafe IT Solutions, J&K, India
                Author notes
                [* ]Corresponding author: Asif Iqbal Khan, Dept. of Computer Science, Jamia Millia Islamia, New Delhi, Mb: +91-9858323226 khanasifiqbal7@ 123456gmail.com
                S0169-2607(20)31414-0 105581
                © 2020 Elsevier B.V. All rights reserved.

                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.

                : 10 April 2020
                : 30 May 2020

                Bioinformatics & Computational biology
                coronavirus,covid-19, pneumonia viral,pneumonia bacterial,convolutional neural network,deep learning


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