[en] The latest advances of deep learning and particularly convolutional neural networks (CNNs) have proven more than once their high accuracy in disease detection. In this chapter, we propose a new deep learning-based approach for COVID-19 detection from chest X-ray images. The proposed approach applies, in an efficient way, the techniques of transfer learning and fine-tuning from pre-trained CNN models (InceptionV3, VGG16, MobileNet, EfficientNet, etc.). The dataset used for our experiments has three classes: normal, COVID-19, and other pathologies. The dataset is split into three sub-sets as follows: 70% for training, 15% for validation, and 15% for the final test. To avoid underfitting or overfitting problems during the training process, we apply regularization techniques (L1 & L2 regularizations, dropout, data augmentation, early stopping, cross-validation, etc.), which help in learning and providing a generalizable solution. As a result, we demonstrate the high efficiency of the proposed CNNs for the detection of COVID-19 from chest X-ray images. A comparison of different architectures shows that VGG16 and MobileNet provide the highest scores: 97.5% and 99.3% of accuracy respectively, 98.7% and 99.3% of sensitivity respectively. In addition, both models provide the scores of 96.3% and 99.2% respectively for specificity. The proposed solution is deployed in the cloud to provide high availability in real time, thanks to a responsive website, and this without the need to download, install, and configure the required libraries.
Disciplines :
Computer science
Author, co-author :
Lessage, Xavier
Mahmoudi, Said
Mahmdoudi, Sidi Ahmed
Laraba, Sohaib
Debauche, Olivier ; Université de Liège - ULiège > TERRA Research Centre
Belarbi, Mohammed Amin
Language :
English
Title :
Chest X-ray Images Analysis with Deep Convolutional Neural Networks (CNN) for COVID-19 Detection
Publication date :
15 March 2021
Main work title :
Healthcare Informatics for Fighting COVID-19 and Future Epidemics
Author, co-author :
Garg, Lalit
Chakraborty, Chinmay
Mahmoudi, Said
Sohmen, Victor
Publisher :
Springer
ISBN/EAN :
978-3-030-72751-2
Collection name :
EAI/Springer Innovations in Communication and Computing
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2818–2826. https://arxiv.org/abs/1512.00567. Cited 6 Aug 2020
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition (2014). https://arxiv.org/abs/1409.1556. Cited 12 Aug 2020
A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: Efficient convolutional neural networks for mobile vision applications (2017). https://arxiv.org/abs/1704.04861. Cited 9 Aug 2020
F. Chollet, Xception: Deep learning with depthwise separable convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017). https://arxiv.org/abs/1610.02357. Cited 8 Aug 2020
G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 4700–4708. https://arxiv.org/abs/1608.06993. Cited 2 Aug 2020
M. Tan, Q.V. Le, EfficientNet: Rethinking model scaling for convolutional neural networks (2019). https://arxiv.org/abs/1905.11946. Cited 5 Aug 2020
H.C. Becker, W.J. Nettleton, P.H. Meyers, J.W. Sweeney, C.M. Nice, Digital computer determination of a medical diagnostic index directly from chest X-ray images. IEEE Trans. Biomed. Eng. 11(3), 67–72 (1964). https://doi.org/10.1109/TBME.1964.4502309
G.S. Lodwick, T.E. Keats, J.P. Dorst, The coding of roentgen images for computer analysis as applied to lung cancer. Radiology 81(2), 185–200 (1963). https://doi.org/10.1148/81.2.185
H.P. Chan, K. Doi, S. Galhotra, C.J. Vyborny, H. MacMahon, P.M. Jokich, Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of micro-calcifications in mammography. Med. Phy. 14(4), 538–548 (1987). https://doi.org/10.1118/1. 596065
M.L. Giger, K. Doi, H. MacMahon, Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med. Phys. 15(2), 158–166 (1988). https://doi.org/10.1118/1.596247
K. Kanazawa, Y. Kawata, N. Niki, et al., Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput. Med. Imaging Graph. 22 157–167 (1998). https://doi. org/10.1016/S0895-6111(98)00017-2
C. Abe, C.E. Kahn, K. Doi, S. Katsuragawa, Computer-aided detection of diffuse liver-disease in ultrasound images. Invest Radiol. 27, 71–77 (1992). https://doi.org/10.1097/00004424-199201000-00015
K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015). https://doi.org/10.1016/j.csbj.2014.11.005
G.F. Cooper, C.F. Aliferis, R. Ambrosino, J. Aronis, B.G. Buchanan, R. Caruana, et al., An evaluation of machine-learning methods for predicting pneumonia mortality. Artif. Intell. Med. 9(2), 107–138 (1997). https://doi.org/10.1016/S0933-3657(96)00367-3
S.Y. Kim, J. Diggans, D. Pankratz, J. Huang, M. Pagan, N. Sindy, et al., Classification of usual interstitial pneumonia in patients with interstitial lung disease: assessment of a machine learning approach using high-dimensional transcriptional data. Lancet Respir. Med. 3(6), 473– 482 (2015). https://doi.org/10.1016/S2213-2600(15)00140-X
R.T. Sousa, O. Marques, F.A.A. Soares, I.I. Sene Jr, L.L. de Oliveira, E.S. Spoto, Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs. Procedia Comput. Sci. 18, 2579–2582 (2013). https://doi.org/10.1016/j.procs.2013.05.444
O. Salem, A. Guerassimov, A. Mehaoua, A. Marcus, B. Furht, Anomaly detection in medical wireless sensor networks using SVM and linear regression models. Int. J. E-Health Med. Commun. 5(1), 20–45 (2014). https://doi.org/10.4018/ijehmc.2014010102
P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A.B. Curtis, C. Langlotz, K. Shpanskaya, M.P. Lungren, A.Y. Ng, CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning (2017). https://arxiv.org/abs/1711.05225. Cited 12 Aug 2020
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, R. Summers, ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in Proceedings of CVPR (2017), pp. 3462–3471
J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, L. Fei-Fei, Imagenet: A large-scale hierarchical image database, in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848
A. Mangal, S. Kalia, H. Rajgopal, K. Rangarajan, V. Namboodiri, S. Banerjee, C. Arora, CovidAID: COVID-19 Detection Using Chest X-Ray (2020). https://arxiv.org/abs/2004.09803. Cited 11 Aug 2020
K. Hammoudi, H. Benhabiles, M. Melkemi, F. Dornaika, I. Arganda-Carreras, D. Collard, A. Scherpereel, Deep Learning on Chest X-ray images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19 (2020). https://hal.archives-ouvertes.fr/hal-025533605. Cited 11 Aug 2020
L. Wang, A. Wong, COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images (2020). https://arxiv.org/abs/2003. 09871. Cited 12 Aug 2020
A.I. Khan, J.L. Shah, M.M. Bhat, Coronet: A deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput. Methods Programs Biomed. 196, 105581 (2020). https://doi.org/10.1016/j.cmpb.2020.105581
E.E.D. Hemdan, M.A. Shouman, M.E. Karar, COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images (2020). https://arxiv.org/abs/2003.11055. Cited 12 Aug 2020
K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks, in European Conference on Computer Vision (Springer, Cham, 2020), pp. 630–645. https://arxiv.org/abs/1603.05027. Cited 10 Aug 2020
C. Szegedy, S. Ioffe, V. Vanhoucke, A.A. Alemi, Inception-v4, inception-resnet and the impact of residual connections on learning, in Thirty-First AAAI Conference on Artificial Intelligence (2017). https://arxiv.org/abs/1602.07261. Cited 10 Aug 2020
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 4510–4520. https://arxiv.org/abs/1801.04381. Cited 10 Aug 2020
I.D. Apostolopoulos, T.A. Mpesiana, Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physic. Eng. Sci. Med. 43 635– 640 (2020). https://doi.org/10.1007/s13246-020-00865-4
A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020). https://arxiv.org/abs/2003. 10849. Cited 12 Aug 2020
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778. https://arxiv.org/abs/1512.03385. Cited 10 Aug 2020
H. Wang, Y. Xia, Chestnet: A deep neural network for classification of thoracic diseases on chest radiography (2018). https://arxiv.org/abs/1807.03058. Cited 13 Aug 2020
A. Haghanifar, M.M. Majdabadi, S. Ko, COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning (2020). https://arxiv.org/abs/2006.13807. Cited 13 Aug 2020
M. Ahishali, A. Degerli, M. Yamac, S. Kiranyaz, M.E. Chowdhury, K. Hameed, T. Hamid, R. Mazhar, M. Gabbouj, A Comparative Study on Early Detection of COVID-19 from Chest X-Ray Images (2020). https://arxiv.org/abs/2006.05332. Cited 7 Aug 2020
K. Elasnaoui, Y. Chawki, Using X-ray images and deep learning for automated detection of coronavirus disease. J. Biomol. Struct. Dyn. 1–22 (2020). https://doi.org/10.1080/07391102. 2020.1767212
D.S. Kermany, K. Zhang, M. Goldbaum, Labeled optical coherence tomography (Oct) and chest X-ray images for classification. Mendeley Data (2018). https://doi.org/10.17632/rscbjbr9sj.2
K. El Asnaoui, Y. Chawki, A. Idri, Automated methods for detection and classification pneumonia based on X-ray images using deep learning (2020). https://arxiv.org/abs/2003. 14363. Cited 13 Aug 2020
