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المؤلفChowdhury, M. E. H.
المؤلفRahman, T.
المؤلفKhandakar, A.
المؤلفMazhar, R.
المؤلفKadir, M.A.
المؤلفMahbub, Z. B.
المؤلفIslam, K. R.
المؤلفKhan, M. S.
المؤلفIqbal, Atif
المؤلفEmadi, N. A.
المؤلفReaz, M. B. I.
المؤلفIslam, M. T.
تاريخ الإتاحة2022-03-31T08:05:54Z
تاريخ النشر2020
اسم المنشورIEEE Access
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/ACCESS.2020.3010287
معرّف المصادر الموحدhttp://hdl.handle.net/10576/29151
الملخصCoronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.
راعي المشروعThis work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors. The publication of this article was funded by the Qatar National Library. The authors would like to thank Italian Society of Medical Radiology and Interventional for sharing the X-ray images of COVID-19 patients publicly and would like to thank J. P. Cohen for taking the initiative to gather images from articles and online resources. Last but not the least, authors would like to acknowledge the Chest X-Ray Images (pneumonia) database and RSNA Pneumonia Detection Challenge in Kaggle which helped significantly to make this work possible. Otherwise, normal and viral pneumonia images were not accessible to the team.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعArtificial intelligence
computer-aided diagnostic tool
COVID-19 pneumonia
machine learning
transfer learning
viral pneumonia
العنوانCan AI Help in Screening Viral and COVID-19 Pneumonia?
النوعArticle
الصفحات132665-132676
رقم المجلد8
dc.accessType Abstract Only


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