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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray

Author(s): Xiangbin Liu, Wenqian Wu, Jerry Chun-Wei Lin* and Shuai Liu*

Volume 19, Issue 4, 2023

Published on: 26 August, 2022

Article ID: e100622205816 Pages: 14

DOI: 10.2174/1573405618666220610093740

Price: $65

Abstract

Background: The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detection and treatment of COVID-19 infected patients are critical to prevent the further spread of the epidemic. However, existing detection methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore, it is crucial to develop a rapid and practical COVID-19 detection method. In this work, we explored the application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method.

Methods: Existing studies have shown that novel coronavirus pneumonia has significant radiographic performance. In this study, we analyze and select the features of chest radiographs. We propose a chest X-Ray (CXR) classification method based on the selected features and investigate the application of transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR classification method based on selected features with transfer learning and ensemble learning and propose an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose pneumonia and COVID-19 using chest x-ray images. The model aims to provide an accurate diagnosis for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/ Pneumonia).

Results: Our proposed CXR classification method based on selection features can significantly improve the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVIDensemble achieved 91.5% accuracy in the binary classification task and 91.11% in the multi-category classification task. The experimental results demonstrate that the COVID-ensemble can quickly and accurately detect COVID-19 and pneumonia automatically through X-ray images and that the performance of this model is superior to that of several existing methods.

Conclusion: Our proposed COVID-ensemble can not only overcome the limitations of the conventional COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection but also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as a fast and efficient screening method for COVID-19 and pneumonia.

Keywords: Deep learning, COVID-19, pneumonia, X-ray, transfer learning, ensemble learning.

Graphical Abstract

[1]
Qqcom. Update on COVID-19. Available from: https://news.qq.com/zt2020/page/feiyan.htm#/global (Accessed on: December 25, 2021).
[2]
Ahuja S, Panigrahi BK, Dey N, Rajinikanth V, Gandhi TK. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell 2021; 51(1): 571-85.
[http://dx.doi.org/10.1007/s10489-020-01826-w] [PMID: 34764547]
[3]
Das AK, Ghosh S, Thunder S, Dutta R, Agarwal S, Chakrabarti A. Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network. Pattern Anal Appl 2021; 24(3): 1111-24.
[http://dx.doi.org/10.1007/s10044-021-00970-4]
[4]
Sr A, Nl B, Aks A, et al. Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images. Chaos Solitons Fractals 2021; 2021: 145.
[5]
Javaheri T, Homayounfar M, Amoozgar Z, et al. CovidCTNet: An open-source deep learning approach to diagnose covid-19 using small cohort of CT images. NPJ Digit Med 2021; 4(1): 29.
[http://dx.doi.org/10.1038/s41746-021-00399-3]
[6]
Yao JC, Wang T, Hou GH, et al. AI detection of mild COVID-19 pneumonia from chest CT scans. Eur Radiol 2021; 31(9): 7192-201.
[http://dx.doi.org/10.1007/s00330-021-07797-x] [PMID: 33738595]
[7]
Chen H, Guo S, Hao Y, et al. Auxiliary diagnosis for COVID-19 with deep transfer learning. J Digit Imaging 2021; 34(2): 231-41.
[http://dx.doi.org/10.1007/s10278-021-00431-8] [PMID: 33634413]
[8]
Wang Z, Dong J, Zhang J. Multi-model ensemble deep learning method to diagnose COVID-19 using chest computed tomography images. J Shanghai Jiaotong Univ 2022; 27(1): 70-80.
[http://dx.doi.org/10.1007/s12204-021-2392-3] [PMID: 34975263]
[9]
Al-antari MA, Hua CH, Bang J, Lee S. Fast deep learning computer-aided diagnosis of COVID-19 based on digital chest x-ray images. Appl Intell 2021; 51(5): 2890-907.
[http://dx.doi.org/10.1007/s10489-020-02076-6] [PMID: 34764573]
[10]
Karar ME, Hemdan EED, Shouman MA. Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans. Complex Intell Sys 2021; 7(1): 235-47.
[http://dx.doi.org/10.1007/s40747-020-00199-4] [PMID: 34777953]
[11]
e L, Zhao B, Guo Y, et al. Using deep‐learning techniques for pulmonary‐thoracic segmentations and improvement of pneumonia diagnosis in pediatric chest radiographs. Pediatr Pulmonol 2019; 54(10): 1617-26.
[http://dx.doi.org/10.1002/ppul.24431] [PMID: 31270968]
[12]
Hu A, Razmjooy N. Brain tumor diagnosis based on metaheuristics and deep learning. Int J Imaging Syst Technol 2021; 31(2): 657-69.
[http://dx.doi.org/10.1002/ima.22495]
[13]
Tian Q, Wu Y, Ren X, Razmjooy N. A New optimized sequential method for lung tumor diagnosis based on deep learning and converged search and rescue algorithm. Biomed Signal Process Control 2021; 68: 102761.
[http://dx.doi.org/10.1016/j.bspc.2021.102761]
[14]
Cai X, Li X, Razmjooy N, Ghadimi N. Breast cancer diagnosis by convolutional neural network and advanced thermal exchange optimization algorithm. Comput Math Methods Med 2021; 2021: 1-13.
[http://dx.doi.org/10.1155/2021/5595180] [PMID: 34790252]
[15]
Guo Z, Xu L, Si Y, Razmjooy N. Novel computer‐aided lung cancer detection based on convolutional neural network ‐based and feature‐based classifiers using metaheuristics. Int J Imaging Syst Technol 2021; 31(4): 1954-69.
[http://dx.doi.org/10.1002/ima.22608]
[16]
Chakraborty M, Dhavale SV, Ingole J. Corona-Nidaan: Lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection. Appl Intell 2021; 51(5): 3026-43.
[http://dx.doi.org/10.1007/s10489-020-01978-9] [PMID: 34764582]
[17]
Anand R, Sowmya V, Menon VK. Modified Vgg deep learning architecture for Covid-19 classification using bio-medical images. IOP Conf Ser Mater Sci Eng. 2021; 1084(1): 9.
[18]
Tiwari S, Jain A. Convolutional capsule network for COVID‐19 detection using radiography images. Int J Imaging Syst Technol 2021; 31(2): 525-39.
[http://dx.doi.org/10.1002/ima.22566] [PMID: 33821095]
[19]
Ingle VA, Ambad PM. CvDeep-COVID-19 detection model. SN Comput Sci 2021; 2(3): 145.
[http://dx.doi.org/10.1007/s42979-021-00531-w] [PMID: 33754141]
[20]
Murugan R, Goel T. E-DiCoNet: Extreme learning machine based classifier for diagnosis of COVID-19 using deep convolutional network. J Ambient Intell Humaniz Comput 2021; 12(9): 8887-98.
[http://dx.doi.org/10.1007/s12652-020-02688-3] [PMID: 33425051]
[21]
P SAB. Annavarapu CSR. Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification. Appl Intell 2021; 51(5): 3104-20.
[http://dx.doi.org/10.1007/s10489-021-02199-4] [PMID: 34764590]
[22]
Altaf F, Islam SMS, Janjua NK. A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays. Neural Comput Appl 2021; 33(20): 14037-48.
[http://dx.doi.org/10.1007/s00521-021-06044-0] [PMID: 33948047]
[23]
Dhaka VS, Rani G, Oza MG, Sharma T, Misra A. A deep learning model for mass screening of COVID ‐19. Int J Imaging Syst Technol 2021; 31(2): 483-98.
[http://dx.doi.org/10.1002/ima.22544] [PMID: 33821094]
[24]
Singh RK, Pandey R, Babu RN. COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays. Neural Comput Appl 2021; 33(14): 8871-92.
[http://dx.doi.org/10.1007/s00521-020-05636-6] [PMID: 33437132]
[25]
Arman S E, Rahman S, Deowan S A. COVIDXception-net: A bayesian optimization based deep learning approach to diagnose COVID-19 from X-ray images. SN Comput Sci 2020; 2(3): 115.
[26]
Paul A, Basu A, Mahmud M, Kaiser MS, Sarkar R. Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays. Neural Comput Appl 2022. [Epub ahead of print].
[http://dx.doi.org/10.1007/s00521-021-06737-6] [PMID: 35013650]
[27]
Verma SS, Prasad A, Kumar A. CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification. Biomed Signal Process Control 2022; 71: 103272.
[http://dx.doi.org/10.1016/j.bspc.2021.103272] [PMID: 34691234]
[28]
Ghosh SK, Ghosh A. ENResNet: A novel residual neural network for chest X-ray enhancement based COVID-19 detection. Biomed Signal Process Control 2022; 72: 103286.
[http://dx.doi.org/10.1016/j.bspc.2021.103286] [PMID: 34745319]
[29]
Albahli S. A deep neural network to distinguish COVID-19 from other chest diseases using X-ray images. Curr Med Imaging Rev 2021; 17(1): 109-19.
[http://dx.doi.org/10.2174/1573405616666200604163954] [PMID: 32496988]
[30]
Dhiman G, Chang V, Singh KK, et al. ADOPT: Automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images. J Biomol Struct Dyn 2022; 40(13): 5836-47.
[31]
Ozturk T, Talo M, Yildirim EA, et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020; 121: 103792.
[http://dx.doi.org/10.1016/j.compbiomed.2020.103792] [PMID: 32568675]
[32]
Cohen JP. COVID-19 image data collection. 2020. Available from: https://github.com/ieee8023/COVID-chestxray-dataset (Accessed on: June 5, 2021).
[33]
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proc IEEE Conf Comput Vision Pattern Recogn 2017. 2017; 2097-106.
[http://dx.doi.org/10.1109/CVPR.2017.369]
[34]
Goceri E. Diagnosis of skin diseases in the era of deep learning and mobile technology. Comput Biol Med 2021; 134: 104458.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104458] [PMID: 34000524]
[35]
Bensaoud A, Kalita J. Deep multi-task learning for malware image classification. J Inform Security Appl 2022; 64: 103057.
[http://dx.doi.org/10.1016/j.jisa.2021.103057]
[36]
El Asnaoui K. Design ensemble deep learning model for pneumonia disease classification. Int J Multimed Inf Retr 2021; 10(1): 55-68.
[http://dx.doi.org/10.1007/s13735-021-00204-7] [PMID: 33643764]
[37]
Evgin G. An application for automated diagnosis of facial dermatological diseases. İzmir İzmir Katip Çelebi Univ Facult. Health Sci J 2021; 6(3): 91-9.
[38]
Goceri E. Deep learning based classification of facial dermatological disorders. Comput Biol Med 2021; 128: 104118.
[http://dx.doi.org/10.1016/j.compbiomed.2020.104118] [PMID: 33221639]
[39]
Xu X, Jiang X, Ma C, et al. Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv Preprint 2020; 2020: 200209334.
[40]
Krishnamoorthy S, Ramakrishnan S, Colaco LB, et al. Comparing a deep learning model’s diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs. Indian J Radiol Imaging 2021; 31 (Suppl. 1): S53-60.
[http://dx.doi.org/10.4103/ijri.IJRI_914_20] [PMID: 33814762]
[41]
Hammoudi K, Benhabiles H, Melkemi M, et al. Deep learning on chest X-ray images to detect and evaluate pneumonia cases at the era of COVID-19. J Med Syst 2021; 45(7): 75.
[http://dx.doi.org/10.1007/s10916-021-01745-4] [PMID: 34101042]
[42]
Sitaula C, Aryal S. New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis. Health Inf Sci Syst 2021; 9(1): 24.
[http://dx.doi.org/10.1007/s13755-021-00152-w] [PMID: 34164119]
[43]
Goceri E. Image augmentation for deep learning based lesion classification from skin images. IEEE 4th Int Conf Image Proces Appl Sys. 2020; 2020: 144-8.
[44]
Singh P, Shankar A. A novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications. J Real-Time Image Process 2021; 18(5): 1711-28.
[http://dx.doi.org/10.1007/s11554-020-01060-0]
[45]
Goceri E. Analysis of capsule networks for image classification. The 15th International Conference on Computer Graphics Vision, Computer Vision and Image Processing. 21st to 23rd July 2021;
[46]
Goceri E. Capsule neural networks in classification of skin lesions. Int Conf Comput Graph Vision Comput Vision Image Proces 2021; 2021: 29-36.
[47]
Goceri E. CapsNet topology to classify tumours from brain images and comparative evaluation. IET Image Process 2020; 14(5): 882-9.
[http://dx.doi.org/10.1049/iet-ipr.2019.0312]
[48]
Redmon J, Farhadi A. YOLO9000: Better, faster Stronger. IEEE Conf Comput Vision Pattern Recogn. 2017; 2017: 6517-25.

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