Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images

Abstract Novel 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..

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Cognitive Computation - 14(2022), 5 vom: 11. Jan., Seite 1752-1772

Sprache:

Englisch

Beteiligte Personen:

Tahir, Anas M. [VerfasserIn]
Qiblawey, Yazan [VerfasserIn]
Khandakar, Amith [VerfasserIn]
Rahman, Tawsifur [VerfasserIn]
Khurshid, Uzair [VerfasserIn]
Musharavati, Farayi [VerfasserIn]
Islam, M. T. [VerfasserIn]
Kiranyaz, Serkan [VerfasserIn]
Al-Maadeed, Somaya [VerfasserIn]
Chowdhury, Muhammad E. H. [VerfasserIn]

Links:

Volltext [lizenzpflichtig]

Themen:

COVID-19 pneumonia
Computer-aided diagnostic tool
Deep convolutional neural networks
MERS
SARS
Transfer learning

Anmerkungen:

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

doi:

10.1007/s12559-021-09955-1

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2131969562

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