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CVDNet: a novel deep learning architecture for detection of coronavirus (Covid-19) from chest X-ray images. (English) Zbl 07508311

Summary: The COVID-19 pandemic is an emerging respiratory infectious disease, also known as coronavirus 2019. It appears in November 2019 in Hubei province (in China), and more specifically in the city of Wuhan, then spreads in the whole world. As the number of cases increases with unprecedented speed, many parts of the world are facing a shortage of resources and testing. Faced with this problem, physicians, scientists and engineers, including specialists in Artificial Intelligence (AI), have encouraged the development of a Deep Learning model to help healthcare professionals to detect COVID-19 from chest X-ray images and to determine the severity of the infection in a very short time, with low cost. In this paper, we propose CVDNet, a Deep Convolutional Neural Network (CNN) model to classify COVID-19 infection from normal and other pneumonia cases using chest X-ray images. The proposed architecture is based on the residual neural network and it is constructed by using two parallel levels with different kernel sizes to capture local and global features of the inputs. This model is trained on a dataset publically available containing a combination of 219 COVID-19, 1341 normal and 1345 viral pneumonia chest x-ray images. The experimental results reveal that our CVDNet. These results represent a promising classification performance on a small dataset which can be further achieve better results with more training data. Overall, our CVDNet model can be an interesting tool to help radiologists in the diagnosis and early detection of COVID-19 cases.

MSC:

68-XX Computer science
92-XX Biology and other natural sciences
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[1] Wu, F.; Zhao, S.; Yu, B., A new coronavirus associated with human respiratory disease in China, Nature, 579, 7798, 265-269 (2020)
[2] Pneumonia of unknown cause-China, Emergencies preparedness, response, disease outbreak news (2020), World Health Organization (WHO)
[3] Zu, Z. Y.; Jiang, M. D.; Xu, P. P.; Chen, W.; Ni, Q. Q.; Lu, G. M., Coronavirus disease 2019 (COVID-19): a perspective from China, Radiology (2020), In press
[4] LeCun, Y.; Bengio, Y.; Hinton, G., Deep learning, Nature, 521, 7553, 436-444 (2015)
[5] Esteva, A.; Kuprel, B.; Novoa, R. A., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 7639, 115-118 (2017)
[6] Yoon, S. H.; Lee, K. H., Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea, Korean J. Radiol., 21, 4, 494-500 (2020)
[7] Talo, M.; Yildirim, O.; Baloglu, U. B.; Aydin, G.; Acharya, U. R., Convolutional neural networks for multi-class brain disease detection using MRI images, . Comput. Med. Imag. Graph, 78, Article 101673 pp. (2019)
[8] Celik, Y.; Talo, M.; Yildirim, O.; Karabatak, M.; Acharya, U. R., Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images, Pattern Recognit. Lett., 133, 232-239 (2020)
[9] Tan, J. H.; Fujita, H.; Sivaprasad, S.; Bhandary, S.; Rao, A. K.; Chua, K. C.; Acharya, U. R., Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network, Inf. Sci., 420, 66-76 (2017)
[10] Rajpurkar P., Irvin J., et al. Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning, 2017 arXiv preprint
[11] Gaal G., Maga B., Lukacs A. Attention U-net based adversarial architectures for chest x-ray lung segmentation, 2020arXiv preprint
[12] Wang L., Lin Z.Q. and Wong A. COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. arXiv, Mar. 2020.
[13] Hemdan E.E., Shouman M.A., Karar M.E. Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in X-ray images. arXiv preprint arXiv:2003.11055. 2020 Mar 24.
[14] Kumar P. and Kumari S. Detection of coronavirus disease (COVID-19) based on deep features. preprints.org, no. March, p. 9, Mar. 2020.
[15] Ozturk, T.; Talo, M.; Yildirim, E. A.; Baloglu, U. B.; Yildirim, O.; Acharya, U. R., Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput Biol Med, Article 103792 pp. (2020)
[16] Ioannis D., Apostolopoulos1, T.B. COVID-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. 2020.
[17] Khan, A. I.; Shah, J. L.; Bhat, M. M., CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images, Comput Methods Programs Biomed, 196, Article 105581 pp. (2020)
[18] Xu X., et al. Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv, Feb. 2020.
[19] Wang S., et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv, p. 2020.02.14.20023028, Apr. 2020.
[20] Li, L.; Qin, L.; Xu, Z.; Yin, Y.; Wang, X.; Kong, B., Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT, Radiology, Article 200905 pp. (2020)
[21] Song Y., Zheng S., Li L., Zhang X., Zhang X., Huang, et al. Deep learning enables accurate diagnosis of novel Coronavirus (COVID-19) with CT images. MedRxiv, 2020.
[22] Ghoshal B. and Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv:2003.10769, 2020.
[23] Zhang J., Xie Y., Li Y., Shen C., and Xia Y. COVID-19 screening on Chest X-ray images using deep learning based anomaly detection. arXiv:2003.12338, 2020.
[24] He, K.; Zhang, X.; Ren, S.; Sun, J., Deep residual learning for image recognition, CVPR (2016)
[25] Kingma, D. P.; Adam, Ba J. L., A method for stochastic optimization, (3rd International Conference on Learning Representations. ICLR 2015 -Conference Track Proceedings (2015))
[26] Rahman T., Chowdhury M.E.H, Khandakar A., Mazhar R., Kadir M.A., Mahbub Z.B., Islam K.R., Khan M.S., Iqbal A., Al‐Emadi N., Ibne Reaz M.B. 2020; COVID‐19 chest radiography database. [Online] Available: https://www.kaggle.com/tawsifurrahman/covid19‐radiography‐database.
[27] Societa Italiana di Radiologia Medical Interventistica. 2020. COVID‐19 database. [Online]. Available: https://www.sirm.org/category/senza-categoria/covid-19/.
[28] Monteral J.C. COVID‐chestxray database. [Online] Available: https://github.com/ieee8023/covid-chestxray-dataset.
[29] Mooney P.Chest X‐ray images (Pneumonia). [Online] Available: https://www.kaggle.com/paultimothymooney/chest-xray-pne umonia 2018.
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