تقرير
Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images
| العنوان: | Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images |
|---|---|
| المؤلفون: | Tahir, Anas, Qiblawey, Yazan, Khandakar, Amith, Rahman, Tawsifur, Khurshid, Uzair, Musharavati, Farayi, Islam, M. T., Kiranyaz, Serkan, Chowdhury, Muhammad E. H. |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | 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). 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. 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. 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. Comment: 10 Figures, 4 Tables |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2005.11524 |
| رقم الأكسشن: | edsarx.2005.11524 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |