Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2020
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| _version_ | 1866911644927393792 |
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| author | Ahishali, Mete Degerli, Aysen Yamac, Mehmet Kiranyaz, Serkan Chowdhury, Muhammad E. H. Hameed, Khalid Hamid, Tahir Mazhar, Rashid Gabbouj, Moncef |
| author_facet | Ahishali, Mete Degerli, Aysen Yamac, Mehmet Kiranyaz, Serkan Chowdhury, Muhammad E. H. Hameed, Khalid Hamid, Tahir Mazhar, Rashid Gabbouj, Moncef |
| contents | Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2006_05332 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Advance Warning Methodologies for COVID-19 using Chest X-Ray Images Ahishali, Mete Degerli, Aysen Yamac, Mehmet Kiranyaz, Serkan Chowdhury, Muhammad E. H. Hameed, Khalid Hamid, Tahir Mazhar, Rashid Gabbouj, Moncef Image and Video Processing Computer Vision and Pattern Recognition Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity. |
| title | Advance Warning Methodologies for COVID-19 using Chest X-Ray Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2006.05332 |