A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images

• Autorzy: Joshi Rakesh Chandra , Yadav Saumya , Pathak Vinay Kumar , Malhotra Hardeep Singh , Khokhar Harsh Vardhan Singh , Parihar Anit , Kohli Neera , Himanshu D. , Garg Ravindra K. , Bhatt Madan Lal Brahma , Kumar Raj , Singh Naresh Pal , Sardana Vijay , Burget Radim , Alippi Cesare , Travieso-Gonzalez Carlos M. , Dutta Malay Kishore

Identyfikatory

DOI

10.1016/j.bbe.2021.01.002

• Języki publikacji: EN

Abstrakty

EN

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

Słowa kluczowe

EN

chest X-ray radiographs; coronavirus; deep learning; image processing; pneumonia

PL

radiogram klatki piersiowej; koronawirus; uczenie głębokie; przetwarzanie obrazu; zapalenie płuc

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 1
• Strony: 239--254
• Opis fizyczny: Bibliogr. 49 poz., rys., tab.

Twórcy

autor

Joshi Rakesh Chandra

• Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, U.P., India

autor

Yadav Saumya

• Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, U.P., India

autor

Pathak Vinay Kumar

• Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, U.P., India

autor

Malhotra Hardeep Singh

• King George's Medical University, Lucknow, U.P., India

autor

Khokhar Harsh Vardhan Singh

• Government Medical College Kota, Rajasthan, India

autor

Parihar Anit

• King George's Medical University, Lucknow, U.P., India

autor

Kohli Neera

• King George's Medical University, Lucknow, U.P., India

autor

Himanshu D.

• King George's Medical University, Lucknow, U.P., India

autor

Garg Ravindra K.

• King George's Medical University, Lucknow, U.P., India

autor

Bhatt Madan Lal Brahma

• King George's Medical University, Lucknow, U.P., India

autor

Kumar Raj

• Uttar Pradesh University of Medical Sciences, Saifai, Etawah, U.P., India

autor

Singh Naresh Pal

• Uttar Pradesh University of Medical Sciences, Saifai, Etawah, U.P., India

autor

Sardana Vijay

• Government Medical College Kota, Rajasthan, India

autor

Burget Radim

• Brno University of Technology, Brno, Czech Republic

autor

Alippi Cesare

• Politecnico di Milano, Milano, Italy; Università della Svizzera Italiana, Lugano, Switzerland

autor

Travieso-Gonzalez Carlos M.

• University of Las Palmas de Gran Canaria (ULPGC), Spain

autor

Dutta Malay Kishore

• Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, U.P., India

Bibliografia

• [1] COVID-19 Dashboard by the Centre for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU)". ArcGIS. Johns Hopkins University. Retrieved 16th September 2020. (Web Link: https://gisanddata.maps.arcgis.com/apps/opsdashboard/ index.html#/bda7594740fd40299423467b48e9ecf6).
• [2] Wu F, Zhao S, Yu B, Chen Y-M, Wang W, Song Z-G, et al. A new coronavirus associated with human respiratory disease in China. Nature 2020;579:265–9. http://dx.doi.org/10.1038/s41586-020-2008-3.
• [3] Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020;395:1054–62. http://dx.doi.org/10.1016/S0140-6736(20)30566-3.
• [4] Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, et al. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 2020;295200463. http://dx.doi.org/10.1148/radiol.2020200463.
• [5] Rodrigues JC, Hare SS, Edey A, Devaraj A, Jacob J, Johnstone A, et al. An update on COVID-19 for the radiologist—a British society of thoracic imaging statement. Clin Radiol 2020;75(May (5)):323–5.
• [6] Bai Y, Yao L, Wei T, Tian F, Jin D-Y, Chen L, et al. Presumed asymptomatic carrier transmission of COVID-19. JAMA 2020;323:1406. http://dx.doi.org/10.1001/jama.2020.2565.
• [7] Whiting P, Singatullina N, Rosser JH. Computed tomography of the chest: I. basic principles. Contin Educ Anaesth Crit Care Pain 2015;15(6):299–304.
• [8] Li M, Lei P, Zeng B, Li Z, Yu P, Fan B, et al. Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease. Acad Radiol 2020;27:603–8. http://dx.doi.org/10.1016/j.acra.2020.03.003.
• [9] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44.
• [10] Ulhaq A, Khan A, Gomes D, Paul M. Computer vision for COVID-19 control: a survey. engrXiv 2020;1–24.
• [11] Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev Biomed Eng 2020;1. http://dx.doi.org/10.1109/RBME.2020.2987975.
• [12] Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv 200310849. 2020. (Zonguldak Bulent Ecevit University, Turkey).
• [13] Zhang J, Xie Y, Li Y, Shen C, Xia Y. COVID-19 screening on chest X-ray images using deep learning based anomaly detection. arXiv 200312338. 2020. (China).
• [14] Khalid, et al. ‘‘Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning’’. https://arxiv.org/abs/2003.14363v1.
• [15] Khan Asif Iqbal, Shah Junaid Latief, Bhat Mohammad Mudasir. CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed 2020105581. http://dx.doi.org/10.1016/j.cmpb.2020.105581. ISSN 0169- 2607.
• [16] Wang Linda, Lin Zhong Qiu, Wong Alexander. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. arXiv 200309871v3 [eess.IV] 15 April 2020.
• [17] Ucar F, Korkmaz D. Covidiagnosis-net: deep bayes- squeezenet based diagnostic of the coronavirus disease 2019 (covid-19) from X-ray images. Med Hypotheses 2020;109761.
• [18] Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv 200310769. 2020. (Brunel University, London, United Kingdom).
• [19] Vaid S, Kalantar R, Bhandari M. Deep learning covid-19 detection bias: accuracy through artificial intelligence. Int Orthop 2020;1.
• [20] Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-rays. Comput Methods Prog Biomed 2020;105608.
• [21] Shi F, Xia L, Shan F, Wu D, Wei Y, Yuan H, et al. Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification. arXiv 200309860. 2020. (China).
• [22] Khobahi S, Agarwal C, Soltanalian M. CoroNet: a deep network architecture for semi-supervised task-based identification of COVID-19 from chest X-ray images. medRxiv )2020;(January). http://dx.doi.org/10.1101/2020.04.14.20065722. 2020.04.14.20065722. (University of Illinois, Chicago).
• [23] Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020103792.
• [24] Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell )2020; (September). http://dx.doi.org/10.1007/s10489-020-01829-7.
• [25] Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Singh V. Application of deep learning for fast detection of covid19 in X-rays using ncovnet. Chaos Solitons Fractals 2020109944.
• [26] Sarker L, Islam MM, Hannan T, Ahmed Z. COVID-DenseNet: a deep learning architecture to detect COVID-19 from chest radiology images. Preprints 2020. http://dx.doi.org/10.20944/preprints202005.0151.v1. 2020050151.
• [27] Yoo SH, Geng H, Chiu TL, Yu SK, Cho DC, Heo J, et al. Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Front Med 2020;7. http://dx.doi.org/10.3389/fmed.2020.00427.
• [28] Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Bhardwaj P, Singh V. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos Solitons Fractals 2020;140(November):110190. http://dx.doi.org/10.1016/j.chaos.2020.110190.
• [29] Apostolopoulos ID, Aznaouridis SI, Tzani MA. Extracting possibly representative covid-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. J Med Biol Eng 2020;1.
• [30] Elasnaoui K, Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 2020;1–22. no. just-accepted.
• [31] Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 2020;1.
• [32] Rahimzadeh M, Attar A. A modified deep convolutional neural network for detecting covid-19 and pneumonia from chest X-ray images based on the concatenation of xception and resnet50v2. Informn Med Unlocked 2020100360.
• [33] Brunese L, Mercaldo F, Reginelli A, Santone A. Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from X-rays. Comput Methods Prog Biomed 2020105608.
• [34] Mahmud T, Rahman MA, Fattah SA. Covxnet: a multidilation convolutional neural network for automatic covid-19 and other pneumonia detection from chest X-ray images with transferable multireceptive feature optimization. Comput Biol Med 2020103869.
• [35] Cohen Joseph Paul, Morrison Paul, Dao Lan. COVID-19 image data collection. arXiv 200311597 2020 https://github.com/ieee8023/COVID-chestxray-dataset.
• [36] Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray- pneumonia.
• [37] Kobayashi Y, Mitsudomi T. Management of ground-glass opacities: should all pulmonary lesions with ground-glass opacity be surgically resected? Transl Lung Cancer Res 2013;2(5):354–63.
• [38] Vilar J, Domingo ML, Soto C, Cogollos J. Radiology of bacterial pneumonia. Eur J Radiol 2004. http://dx.doi.org/10.1016/j.ejrad.2004.03.010.
• [39] Wong HYF, Lam HYS, Fong AH-T, Leung ST, Chin TW-Y, Lo CSY, et al. Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology 2020;296: E72–8. http://dx.doi.org/10.1148/radiol.2020201160.
• [40] Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. Am J Roentgenol 2020;215:87–93. http://dx.doi.org/10.2214/AJR.20.23034.
• [41] Alom MZ, Taha TM, Yakopcic C, Westberg S, Hasan SM, Van Esesn BC, et al. The history began from AlexNet: a comprehensive survey on deep learning approaches. arXiv preprintarXiv 20181803.01164.
• [42] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88.
• [43] Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems; 2012. p. 1097–105.
• [44] Redmon J, Farhadi A. Yolov3: an incremental improvement. arXiv preprint arXiv 2018. 1804.02767.
• [45] Lin T-Y, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. http://dx.doi.org/10.1109/cvpr.2017.106.
• [46] Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. MobileNetV2: inverted residuals and linear bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2018. http://dx.doi.org/10.1109/CVPR.2018.00474.
• [47] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings; 2015.
• [48] Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017. http://dx.doi.org/10.1109/TPAMI.2016.2577031.
• [49] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2016. http://dx.doi.org/10.1109/CVPR.2016.90.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).