Deep Learning Classififcation For Diagnosis COVID-19 Between Bacterial Pneumonia and Viral Pneumonia in Chest X-Ray Images
Abstract
The novel coronavirus disease (COVID-19) was identified in the city of Wuhan, China at the end of 2019 as novel illness pneumonia. Today, it's being an epidemic around the world, the amount of sick people and fatalities increases growing increasingly every day according to the World Health Organisation (WHO) revised statistics. The purpose of this article is therefore to incorporate a new deep learning Images Classifiers, to diagnose COVID-19 and differentiate it between (Normal, Bacterial Pneumonia, Viral Pneumonia) in X-ray Images. The study is validated on 2002 Chest X-ray images with 60 confirmed positive COVID-19 cases and (650 x-rays bacterial pneumonia, 412 x-ray viral pneumonia, 880 normal x-rays) images. The proposed architectures of the deep convolutional neural network model (DCNN-COVID-NET) can analyze the normalized intensities of the X-ray image to classify the patient status as (normal,bacterial , viral , COVID-19) pneumonia case, In comparison with (VGG19) and (DenseNet, ResNetV2, InceptionV3, InceptionResNetV2, Xception, MobileNetV2) of deep convolutional neural network models. Experiments and evaluation of the proposed (DCNN-COVID-NET) have been successfully done based on 80-20% of Xray images for the model training and testing phases, respectively. The (DCNN-COVID-NET) Convolutional Network model showed a good performance of automated COVID-19 classification with f1-scores of 1.00 and 0.98 for normal and COVID-19, respectively among other deep convolutional neural network models. This study demonstrated the useful deep learning model to classify COVID-19 in chest X-ray images based on the proposed (DCNN-COVID-NET).
Keywords:
Bacterial -Viral Pneumonia, COVID-19, X-ray Image, Deep Learning, Convolution Neural NetworkDownloads
References
C. I. Paules, H. D. Marston, and A. S. Fauci, "Coronavirus Infections—More Than Just the Common Cold," JAMA, vol. 323, no. 8, pp. 707-708, 2020.
Y. Chen, Q. Liu, and D. Guo, "Emerging coronaviruses: Genome structure, replication, and pathogenesis," Journal of Medical Virology, vol. 92, no. 4, pp. 418-423, 2020.
N. Zhu et al., "A Novel Coronavirus from Patients with Pneumonia in China, 2019," New England Journal of Medicine, vol. 382, no. 8, pp. 727-733, 2020.
Q. Li et al., "Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia," New England Journal of Medicine, 2020.
J. Cohen and D. Normile, "New SARS-like virus in China triggers alarm," Science, vol. 367, no. 6475, pp. 234-235, 2020.
T. Lupia, S. Scabini, S. Mornese Pinna, G. Di Perri, F. G. De Rosa, and S. Corcione, "2019 novel coronavirus (2019-nCoV) outbreak: A new challenge," Journal of Global Antimicrobial Resistance, vol. 21, pp. 22-27, 2020/06/01/ 2020.
C. Sohrabi et al., "World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)," International Journal of Surgery, vol. 76, pp. 71-76, 2020/04/01/ 2020.
https://www.who.int/emergencies/diseases/novel-coronavirus-2019/media-resources/press-briefings.
P. Huang et al., "Use of Chest CT in Combination with Negative RT-PCR Assay for the 2019 Novel Coronavirus but High Clinical Suspicion," Radiology, vol. 295, no. 1, pp. 22-23, 2020.
M.-Y. Ng et al., "Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review," Radiology: Cardiothoracic Imaging, vol. 2, no. 1, p. e200034, 2020.
H. Liu, F. Liu, J. Li, T. Zhang, D. Wang, and W. Lan, "Clinical and CT imaging features of the COVID19 pneumonia: Focus on pregnant women and children," Journal of Infection, 2020/03/21/ 2020.
M. Chung et al., "CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)," Radiology, vol. 295, no. 1, pp. 202-207, 2020.
L. J. M. Kroft, L. van der Velden, I. H. Girón, J. J. H. Roelofs, A. de Roos, and J. Geleijns, "Added Value of Ultra–low-dose Computed Tomography, Dose Equivalent to Chest X-Ray Radiography, for Diagnosing Chest Pathology," Journal of Thoracic Imaging, vol. 34, no. 3, pp. 179-186, 2019.
N. Chen et al., "Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study," Lancet, vol. 395, no. 10223, pp. 507-513, Feb 15 2020.
M. E. Karar, D. R. Merk, V. Falk, and O. Burgert, "A simple and accurate method for computer-aided transapical aortic valve replacement," Computerized Medical Imaging and Graphics, vol. 50, pp. 31-41, 2016.
D. R. Merk et al., "Image-guided transapical aortic valve implantation: sensorless tracking of stenotic valve landmarks in live fluoroscopic images," (in eng), Innovations, vol. 6, no. 4, pp. 231-6, Jul 2011.
M. Messerli et al., "Computer-aided detection (CAD) of solid pulmonary nodules in chest x-ray equivalent ultralow dose chest CT - first in-vivo results at dose levels of 0.13mSv," European Journal of Radiology, vol. 85, no. 12, pp. 2217-2224, 2016/12/01/ 2016.
X. Tian et al., "Medical imaging and diagnosis of subpatellar vertebrae based on improved Laplacian image enhancement algorithm," Computer Methods and Programs in Biomedicine, vol. 187, p. 105082, 2020/04/01/ 2020.
T. He, J. Hu, Y. Song, J. Guo, and Z. Yi, "Multi-task learning for the segmentation of organs at risk with label dependence," Medical Image Analysis, vol. 61, p. 101666, 2020/04/01/ 2020.
R. Hannan, M. Free, V. Arora, R. Harle, and P. Harvie, "Accuracy of computer navigation in total knee arthroplasty: A prospective computed tomography-based study," Medical Engineering & Physics, 2020/03/04/ 2020.
C. H. Liang, Y. C. Liu, M. T. Wu, F. Garcia-Castro, A. Alberich-Bayarri, and F. Z. Wu, "Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice," Clinical Radiology, vol. 75, no. 1, pp. 38-45, 2020/01/01/ 2020.
S. Hussein, P. Kandel, C. W. Bolan, M. B. Wallace, and U. Bagci, "Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches," IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1777-1787, 2019.
M. E. Karar, S. H. El-Khafif, and M. A. El-Brawany, "Automated Diagnosis of Heart Sounds Using Rule-Based Classification Tree," Journal of Medical Systems, journal article vol. 41, no. 4, p. 60, March 01 2017.
M. E. Karar, D. R. Merk, C. Chalopin, T. Walther, V. Falk, and O. Burgert, "Aortic valve prosthesis tracking for transapical aortic valve implantation," (in Eng), Int J Comput Assist Radiol Surg, vol. 6, no. 5, pp. 583-590, Sep 16 2011.
N. Ghassemi, A. Shoeibi, and M. Rouhani, "Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images," Biomedical Signal Processing and Control, vol. 57, p. 101678, 2020/03/01/ 2020.
H. Rathore, A. K. Al-Ali, A. Mohamed, X. Du, and M. Guizani, "A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants," IEEE Access, vol. 7, pp. 24154-24164, 2019.
L. Chen, P. Bentley, K. Mori, K. Misawa, M. Fujiwara, and D. Rueckert, "Self-supervised learning for medical image analysis using image context restoration," Medical Image Analysis, vol. 58, p. 101539, 2019/12/01/ 2019.
F. Gao, H. Yoon, T. Wu, and X. Chu, "A feature transfer enabled multi-task deep learning model on medical imaging," Expert Systems with Applications, vol. 143, p. 112957, 2020/04/01/ 2020.
M. Kim, C. Yan, D. Yang, Q. Wang, J. Ma, and G. Wu, "Chapter Eight - Deep learning in biomedical image analysis," in Biomedical Information Technology (Second Edition), D. D. Feng, Ed.: Academic Press, 2020, pp. 239-263.
D.-X. Zhou, "Theory of deep convolutional neural networks: Downsampling," Neural Networks, vol. 124, pp. 319-327, 2020/04/01/ 2020.
T. K. K. Ho, J. Gwak, O. Prakash, J.-I. Song, and C. M. Park, "Utilizing Pretrained Deep Learning Models for Automated Pulmonary Tuberculosis Detection Using Chest Radiography," in Intelligent Information and Database Systems, Cham, 2019, pp. 395-403: Springer International Publishing.
F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers, and D. Pfeiffer, "Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization," Scientific Reports, vol. 9, no. 1, p. 6268, 2019/04/18 2019.
Y. Miki et al., "Classification of teeth in cone-beam CT using deep convolutional neural network," Computers in Biology and Medicine, vol. 80, pp. 24-29, 2017/01/01/ 2017.
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," 2014.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 vol. 2017-January, ed: Institute of Electrical and Electronics Engineers Inc., 2017, pp. 2261-2269.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818-2826.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770-778, 2016.
E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, "A comparative study of fine-tuning deep learning models for plant disease identification," Computers and Electronics in Agriculture, vol. 161, pp. 272279, 2019/06/01/ 2019.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, "Inception-v4, inception-ResNet and the impact of residual connections on learning," presented at the Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 2017.
F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 vol. 2017-January, ed: Institute of Electrical and Electronics Engineers Inc., 2017, pp. 1800-1807.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018.
S. L. Harris and D. M. Harris, "3 - Sequential Logic Design," in Digital Design and Computer Architecture, S. L. Harris and D. M. Harris, Eds. Boston: Morgan Kaufmann, 2016, pp. 108-171.
M. Sokolova and G. Lapalme, "A systematic analysis of performance measures for classification tasks," Information Processing & Management, vol. 45, no. 4, pp. 427-437, 2009/07/01/ 2009.
A. Gulli, A. Kapoor, and S. Pal, Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition. Packt Publishing, 2019.
https://www.worldometers.info/coronavirus/
Ezz El-Din Hemdan1 , Marwa A. Shouman1 , and Mohamed Esmail Karar ,” COVIDX-Net : A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images” arXiv preprint arXiv:2003.11055?.
Gamal M. I.Selim Dr Hatem Khater, A. A. F. (2020). A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer Detection. 2nd International Conference on Advanced Research in Applied Science and Engineering , Which Will Be Held in Oxford, UK.
Gamal M. I.Selim Dr Hatem Khater, E. A. A. F. (2020). Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease. The 9th International Conference on Research in Science and Technology (RSTCONF), 20-22 March 2020 in Berlin, Germany .
Gamal M. I.Selim Dr Hatem Khater, E. A. A. F. (2020). A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19). (International Journal of Scientific and Engineering Research (IJSER) - (ISSN 2229-5518, 11(3), 9.
Gamal M. I.Selim Dr Hatem Khater, E. A. A. F. (2020). Applying Artificial Intelligence Techniques for Prediction of Neurodegenerative Disorders: A Comparative Case-Study on Clinical Tests and Neuroimaging Tests with Alzheimer’s Disease. 2nd International Conference on Advanced Research in Applied Science and Engineering , Which Will Be Held in Oxford, UK.
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