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المؤلفTahir A.M.
المؤلفQiblawey Y.
المؤلفKhandakar A.
المؤلفRahman T.
المؤلفKhurshid U.
المؤلفMusharavati F.
المؤلفIslam M.T.
المؤلفKiranyaz, Mustafa Serkan
المؤلفAl-Maadeed S.
المؤلفChowdhury M.E.H.
تاريخ الإتاحة2022-04-26T12:31:17Z
تاريخ النشر2022
اسم المنشورCognitive Computation
المصدرScopus
المعرّفhttp://dx.doi.org/10.1007/s12559-021-09955-1
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122888534&doi=10.1007%2fs12559-021-09955-1&partnerID=40&md5=c945051114b222460215af8b26edc8c8
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30578
الملخصNovel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.
اللغةen
الناشرSpringer
الموضوعBiological organs
Computer aided diagnosis
Computer aided instruction
Convolution
Convolutional neural networks
Coronavirus
Image classification
Image segmentation
Transfer learning
Chest X-ray image
Computer aided diagnostics
Computer-aided diagnostic tool
Coronaviruses
COVID-19 pneumonia
Diagnostics tools
Middle East
Middle east respiratory syndrome
Severe acute respiratory syndrome
Transfer learning
Deep neural networks
العنوانDeep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images
النوعArticle
dc.accessType Abstract Only


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