142
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

A systematic review: Chest radiography images (X-ray images) analysis and COVID-19 categorization diagnosis using artificial intelligence techniques

&
Pages 26-64 | Received 14 Mar 2022, Accepted 08 Nov 2022, Published online: 24 Nov 2022

References

  • Abbas A, Abdelsamea MM, Gaber MM. 2021. Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network. Appl Intell. 51(2):854–864. doi:10.1007/s10489-020-01829-7.
  • Aggarwal S, Gupta S, Alhudhaif A, Koundal D, Gupta R, Polat K. 2022. Automated covid‐19 detection in chest x‐ray images using fine‐tuned deep learning architectures. Expert Syst. 39(3):e12749. doi:10.1111/exsy.12749.
  • Ahammed K, Satu MS, Abedin MZ, Rahaman MA, Islam SMS. 2020. Early detection of coronavirus cases using chest x-ray images employing machine learning and deep learning approaches. medRxiv.
  • Almalki YE, Qayyum A, Irfan M, Haider N, Glowacz A, Alshehri FM, Alduraibi SK, Alshamrani K, Alkhalik Basha MA, Alduraibi A, et al. 2021. A novel method for covid-19 diagnosis using artificial intelligence in chest x-ray images. Healthcare. 9:522. Multidisciplinary Digital Publishing Institute. doi:10.3390/healthcare9050522.
  • Apostolopoulos ID, Aznaouridis SI, Tzani MA. 2020. 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. 1.
  • Aradhya VM, Mahmud M, Guru D, Agarwal B, Kaiser MS. 2021. One-shot cluster-based approach for the detection of covid–19 from chest x–ray images. Cognit Comput. 1–9.
  • Asif S, Wenhui Y, Jin H, Tao Y, Jinhai S. 2020. Classification of covid-19 from chest x-ray images using deep convolutional neural networks. MedRxiv.
  • Asnaoui KE, Chawki Y, Idri A. 2020. Automated methods for detection and classification pneumonia based on x-ray images using deep learning. arXiv preprint arXiv. 200314363.
  • Ausawalaithong W, Thirach A, Marukatat S, Wilaiprasitporn T. Automatic lung cancer prediction from chest x-ray images using the deep learning approach. 2018 11th Biomedical Engineering International Conference (BMEICON); 2018. Chiang Mai, Thailand: IEEE.
  • Ayan E, Ünver HM. 2019. Diagnosis of pneumonia from chest x-ray images using deep learning. In: 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT). Istanbul, Turkey: IEEE; p. 1–5. doi:10.1109/EBBT.2019.8741582.
  • Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A. 2019. Comparison of deep learning approaches for multi-label chest x-ray classification. Sci Rep. 9(1):1–10. doi:10.1038/s41598-019-42294-8.
  • Bassi PR, Attux R. 2021. A deep convolutional neural network for covid-19 detection using chest x-rays. Biomed Eng Res. 1–10.
  • Basu S, Mitra S, Saha N. Deep learning for screening covid-19 using chest x-ray images. 2020 IEEE Symposium Series on Computational Intelligence (SSCI); 2020: Canberra, ACT, Australia: IEEE.
  • Bhandary A, Prabhu GA, Rajinikanth V, Thanaraj KP, Satapathy SC, Robbins DE, Shasky C, Zhang Y-D, Tavares JMR, Raja NSM. 2020. Deep-learning framework to detect lung abnormality–a study with chest x-ray and lung ct scan images. Pattern Recognit Lett. 129:271–278. doi:10.1016/j.patrec.2019.11.013.
  • Bhattacharyya A, Bhaik D, Kumar S, Thakur P, Sharma R, Pachori RB. 2022. A deep learning based approach for automatic detection of covid-19 cases using chest x-ray images. Biomed Signal Process Control. 71:103182. doi:10.1016/j.bspc.2021.103182.
  • Blain M, Kassin MT, Varble N, Wang X, Xu Z, Xu D, Carrafiello G, Vespro V, Stellato E, Ierardi AM. 2021. Determination of disease severity in covid-19 patients using deep learning in chest x-ray images. Diagn Interv Radiol. 27(1):20. doi:10.5152/dir.2020.20205.
  • Brahim A, Jennane R, Riad R, Janvier T, Khedher L, Toumi H, Lespessailles E. 2019. A decision support tool for early detection of knee osteoarthritis using x-ray imaging and machine learning: data from the osteoarthritis initiative. Comput Med Imaging Graph. 73:11–18. doi:10.1016/j.compmedimag.2019.01.007.
  • Brunese L, Mercaldo F, Reginelli A, Santone A. 2020. Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-rays. Comput Methods Programs Biomed. 196:105608. doi:10.1016/j.cmpb.2020.105608.
  • Bullock J, Cuesta-Lázaro C, Quera-Bofarull A. 2019. Xnet: a convolutional neural network (cnn) implementation for medical x-ray image segmentation suitable for small datasets. In: Medical imaging 2019: biomedical applications in molecular, structural, and functional imaging. San Diego, California, United States: International Society for Optics and Photonics; p. 453–463.
  • Burlacu A, Crisan-Dabija R, Popa IV, Artene B, Birzu V, M P, C P, D G. 2020. Curbing the ai-induced enthusiasm in diagnosing covid-19 on chest x-rays: the present and the near-future. medRxiv.
  • Candemir S, Antani S. 2019. A review on lung boundary detection in chest x-rays. Int J Comput Assist Radiol Surg. 14(4):563–576. doi:10.1007/s11548-019-01917-1.
  • Castiglioni I, Ippolito D, Interlenghi M, Monti CB, Salvatore C, Schiaffino S, Polidori A, Gandola D, Messa C, Sardanelli F. 2021. Machine learning applied on chest x-ray can aid in the diagnosis of covid-19: a first experience from lombardy, Italy. Eur Radiol Exp. 5(1):1–10. doi:10.1186/s41747-020-00203-z.
  • Chaudhary Y, Mehta M, Sharma R, Gupta D, Khanna A, Rodrigues JJ. Efficient-covidnet: deep learning based covid-19 detection from chest x-ray images. 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM), Shenzhen, China; 2021: IEEE; p. 1–6. doi:10.1109/HEALTHCOM49281.2021.9398980.
  • Dai W, Dong N, Wang Z, Liang X, Zhang H, Xing EP. 2018. Scan: structure correcting adversarial network for organ segmentation in chest x-rays. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer; p. 263–273.
  • Das NN, Kumar N, Kaur M, Kumar V, Singh D. 2020b. Automated deep transfer learning-based approach for detection of covid-19 infection in chest x-rays. Irbm.
  • Das D, Santosh K, Pal U. 2020a. Truncated inception net: covid-19 outbreak screening using chest x-rays. Phys Eng Sci Med. 43(3):915–925. doi:10.1007/s13246-020-00888-x.
  • Degerli A, Ahishali M, Yamac M, Kiranyaz S, Chowdhury ME, Hameed K, Hamid T, Mazhar R, Gabbouj M. 2021. Covid-19 infection map generation and detection from chest x-ray images. Health Inf Sci Syst. 9(1):1–16. doi:10.1007/s13755-021-00146-8.
  • Elaziz MA, Hosny KM, Salah A, Darwish MM, Lu S, Sahlol AT, Damasevicius R. 2020. New machine learning method for image-based diagnosis of covid-19. Plos one. 15(6):e0235187. doi:10.1371/journal.pone.0235187.
  • Gordienko Y, Gang P, Hui J, Zeng W, Kochura Y, Alienin O, Rokovyi O, Stirenko S. Deep learning with lung segmentation and bone shadow exclusion techniques for chest x-ray analysis of lung cancer. International Conference on Computer Science, Engineering and Education Applications; 2018: Cham: Springer.
  • Gozes O, Frid-Adar M, Sagie N, Zhang H, Ji W, Greenspan H. 2020. Coronavirus detection and analysis on chest ct with deep learning. arXiv preprint arXiv:200402640.
  • Hatt M, Parmar C, Qi J, El Naqa I. 2019. Machine (deep) learning methods for image processing and radiomics. IEEE Trans Radiat Plasma Med Sci. 3(2):104–108.
  • Heidari M, Mirniaharikandehei S, Khuzani AZ, Danala G, Qiu Y, Zheng B. 2020. Improving the performance of cnn to predict the likelihood of covid-19 using chest x-ray images with preprocessing algorithms. Int J Med Inform. 144:104284. doi:10.1016/j.ijmedinf.2020.104284.
  • Hwang EJ, Kim H, Yoon SH, Goo JM, Park CM. 2020. Implementation of a deep learning-based computer-aided detection system for the interpretation of chest radiographs in patients suspected for covid-19. Korean J Radiol. 21(10):1150. doi:10.3348/kjr.2020.0536.
  • Irfan M, Iftikhar MA, Yasin S, Draz U, Ali T, Hussain S, Bukhari S, Alwadie AS, Rahman S, Glowacz A. 2021. Role of hybrid deep neural networks (hdnns), computed tomography, and chest x-rays for the detection of covid-19. Int J Environ Res Public Health. 18(6):3056. doi:10.3390/ijerph18063056.
  • Jain R, Gupta M, Taneja S, Hemanth DJ. 2021. Deep learning based detection and analysis of covid-19 on chest x-ray images. Appl Intell. 51(3):1690–1700. doi:10.1007/s10489-020-01902-1.
  • Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJ. 2019. Identifying pneumonia in chest x-rays: a deep learning approach. Measurement. 145:511–518. doi:10.1016/j.measurement.2019.05.076.
  • Kassani SH, Kassasni PH, Wesolowski MJ, Schneider KA, Deters R. 2020. Automatic detection of coronavirus disease (covid-19) in x-ray and ct images: a machine learning-based approach. arXiv preprint arXiv:200410641.
  • Kaur P, Singh G, Kaur P. 2018. A review of denoising medical images using machine learning approaches. Curr Med Imaging. 14(5):675–685. doi:10.2174/1573405613666170428154156.
  • Khalid H, Hussain M, Al Ghamdi MA, Khalid T, Khalid K, Khan MA, Fatima K, Masood K, Almotiri SH, Farooq MS. 2020. A comparative systematic literature review on knee bone reports from mri, x-rays and ct scans using deep learning and machine learning methodologies. Diagnostics. 10(8):518. doi:10.3390/diagnostics10080518.
  • Khuzani AZ, Heidari M, Shariati SA. 2021. Covid-classifier: an automated machine learning model to assist in the diagnosis of covid-19 infection in chest x-ray images. Sci Rep. 11(1):1–6. doi:10.1038/s41598-020-79139-8.
  • Kumar R, Arora R, Bansal V, Sahayasheela VJ, Buckchash H, Imran J, Narayanan N, Pandian GN, Raman B. 2020. Accurate prediction of covid-19 using chest x-ray images through deep feature learning model with smote and machine learning classifiers. MedRxiv.
  • Lai Z, Chen Q. 2019. Reconstructing granular particles from x-ray computed tomography using the tws machine learning tool and the level set method. Acta Geotechnica. 14(1):1–18. doi:10.1007/s11440-018-0759-x.
  • Liao R, Rubin J, Lam G, Berkowitz S, Dalal S, Wells W, Horng S, Golland P. 2019. Semi-supervised learning for quantification of pulmonary edema in chest x-ray images. arXiv preprint arXiv:190210785.
  • Loey M, El-Sappagh S, Mirjalili S. 2022. Bayesian-based optimized deep learning model to detect covid-19 patients using chest x-ray image data. Comput Biol Med. 142:105213. doi:10.1016/j.compbiomed.2022.105213.
  • Luz E, Silva P, Silva R, Silva L, Guimarães J, Miozzo G, Moreira G, Menotti D. 2021. Towards an effective and efficient deep learning model for covid-19 patterns detection in x-ray images. Biomed Eng Res. 1–14.
  • Luz E, Silva P, Silva R, Silva L, Guimarães J, Miozzo G, Moreira G, Menotti D. 2022. Towards an effective and efficient deep learning model for covid-19 patterns detection in x-ray images. Biomed Eng Res. 38(1):149–162. doi:10.1007/s42600-021-00151-6.
  • Madani A, Moradi M, Karargyris A, Syeda-Mahmood T. 2018. Chest x-ray generation and data augmentation for cardiovascular abnormality classification. In: Medical imaging 2018: image processing. Houston, Texas, United States: International Society for Optics and Photonics.
  • Maghded HS, Ghafoor KZ, Sadiq AS, Curran K, Rawat DB, Rabie K. A novel ai-enabled framework to diagnose coronavirus covid-19 using smartphone embedded sensors: design study. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA; 2020: IEEE.
  • Maghdid HS, Asaad AT, Ghafoor KZ, Sadiq AS, Mirjalili S, Khan MK. 2021. Diagnosing covid-19 pneumonia from x-ray and ct images using deep learning and transfer learning algorithms. In: Multimodal image exploitation and learning. International Society for Optics and Photonics. (vol. 11734, pp. 99–110).
  • Mohammed MA, Abdulkareem KH, Garcia-Zapirain B, Mostafa SA, Maashi MS, Al-Waisy AS, Subhi MA, Mutlag AA, Le DN. 2020. A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of covid-19 based on x-ray images. Comput Mater Contin. 66(3).
  • Mondal S, Agarwal K, Rashid M. Deep learning approach for automatic classification of x-ray images using convolutional neural network. 2019 Fifth International Conference on Image Information Processing (ICIIP), Shimla, India; 2019: IEEE.
  • Nam JG, Park S, Hwang EJ, Lee JH, Jin K-N, Lim KY, Vu TH, Sohn JH, Hwang S, Goo JM. 2019. Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology. 290(1):218–228. doi:10.1148/radiol.2018180237.
  • Nasution MK, Elveny M, Hardi SM, Jaya I, Siregar MA. Identification of tuberculosis (tb) disease based on lung x-rays using extreme learning machine. Journal of Physics: Conference Series, Medan, Indonesia; 2020: IOP Publishing.
  • Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Singh V. 2020. Application of deep learning for fast detection of covid-19 in x-rays using ncovnet. Chaos Solitons Fractals. 138:109944. doi:10.1016/j.chaos.2020.109944.
  • Pham TD. 2021. Classification of covid-19 chest x-rays with deep learning: new models or fine tuning? Health Inf Sci Syst. 9(1):1–11. doi:10.1007/s13755-020-00135-3.
  • Putha P, Tadepalli M, Reddy B, Raj T, Chiramal JA, Govil S, Sinha N, M KS, Reddivari S, Jagirdar A 2018. Can artificial intelligence reliably report chest x-rays?: radiologist validation of an algorithm trained on 2.3 million x-rays. arXiv preprint arXiv:180707455.
  • Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR, Antani SK. 2020. Iteratively pruned deep learning ensembles for covid-19 detection in chest x-rays. IEEE Access. 8:115041–115050. doi:10.1109/ACCESS.2020.3003810.
  • Rasheed J, Hameed AA, Djeddi C, Jamil A, Al-Turjman F. 2021. A machine learning-based framework for diagnosis of covid-19 from chest x-ray images. Interdiscip Sci. 13(1):103–117.
  • Rashidi HH, Tran NK, Betts EV, Howell LP, Green R. 2019. Artificial intelligence and machine learning in pathology: the present landscape of supervised methods. Acad Pathol. 6:2374289519873088. doi:10.1177/2374289519873088.
  • Razzak MI, Naz S, Zaib A. 2018. Deep learning for medical image processing: overview, challenges and the future. Classif BioApps. 323–350.
  • Saiz FA, Barandiaran I. 2020. Covid-19 detection in chest x-ray images using a deep learning approach. Int J Interact Multi Artifi Intell:1. InPress (InPress).
  • Sedik A, Hammad M, El-Samie FE A, Gupta BB, Abd El-Latif AA. 2021. Efficient deep learning approach for augmented detection of coronavirus disease. Neural Comput Appl. 1–18.
  • Selvan R, Dam EB, Detlefsen NS, Rischel S, Sheng K, Nielsen M, Pai A. Lung segmentation from chest x-rays using variational data imputation. arXiv preprint arXiv:2005.10052
  • Shelke A, Inamdar M, Shah V, Tiwari A, Hussain A, Chafekar T, Mehendale N. 2021. Chest x-ray classification using deep learning for automated covid-19 screening. SN Comput Sci. 2(4):1–9. doi:10.1007/s42979-021-00695-5.
  • Silva P, Luz E, Silva G, Moreira G, Silva R, Lucio D, Menotti D. 2020. Covid-19 detection in ct images with deep learning: a voting-based scheme and cross-datasets analysis. Inform Med Unlocked. 20:100427. doi:10.1016/j.imu.2020.100427.
  • Singh RK, Pandey R, Babu RN. 2021. Covidscreen: explainable deep learning framework for differential diagnosis of covid-19 using chest x-rays. Neural Comput Appl 1-22.
  • Sinha N, Karjee P, Agrawal R, Banerjee A, Pradhan C. 2022. Covid-19 recommendation system of chest x-ray images using cnn deep learning technique with optimizers and activation functions. In: Understanding covid-19: the role of computational intelligence. Cham: Springer; p. 141–163.
  • Suzuki K. 2017. Overview of deep learning in medical imaging. Radiol Phys Technol. 10(3):257–273. doi:10.1007/s12194-017-0406-5.
  • Tan T, Das B, Soni R, Fejes M, Yang H, Ranjan S, Szabo DA, Melapudi V, Shriram K, Agrawal U. 2022. Multi-modal trained artificial intelligence solution to triage chest x-ray for covid-19 using pristine ground-truth, versus radiologists. Neurocomputing. 485:36–46. doi:10.1016/j.neucom.2022.02.040.
  • Tataru C, Yi D, Shenoyas A, Ma A. Deep learning for abnormality detection in chest x-ray images. IEEE Conference on Deep Learning, Erode, India; 2017.
  • Taylor AG, Mielke C, Mongan J, Saria S. 2018. Automated detection of moderate and large pneumothorax on frontal chest x-rays using deep convolutional neural networks: a retrospective study. PLoS Med. 15(11):e1002697. doi:10.1371/journal.pmed.1002697.
  • Thamilarasi V, Roselin R. Automatic classification and accuracy by deep learning using cnn methods in lung chest x-ray images. Materials Science and Engineering Conference Series, Erode, India; 2021.
  • This H, Nam JG, Lim WH, Park SJ, Jeong YS, Kang JH, Hong EK, Kim TM, Goo JM, Park S. 2019. Deep learning for chest radiograph diagnosis in the emergency department. Radiology. 293:573–580. doi:10.1148/radiol.2019191225.
  • Toğaçar M, Ergen B, Cömert Z. 2020a. Covid-19 detection using deep learning models to exploit social mimic optimization and structured chest x-ray images using fuzzy color and stacking approaches. Comput Biol Med. 121:103805. doi:10.1016/j.compbiomed.2020.103805.
  • Toğaçar M, Ergen B, Cömert Z, Özyurt F. 2020b. A deep feature learning model for pneumonia detection applying a combination of mrmr feature selection and machine learning models. Irbm. 41(4):212–222. doi:10.1016/j.irbm.2019.10.006.
  • Unberath M, Zaech J-N, Gao C, Bier B, Goldmann F, Lee SC, Fotouhi J, Taylor R, Armand M, Navab N. 2019. Enabling machine learning in x-ray-based procedures via realistic simulation of image formation. Int J Comput Assist Radiol Surg. 14(9):1517–1528. doi:10.1007/s11548-019-02011-2.
  • Varela-Santos S, Melin P. 2021. A new approach for classifying coronavirus covid-19 based on its manifestation on chest x-rays using texture features and neural networks. Inf Sci (Ny). 545:403–414. doi:10.1016/j.ins.2020.09.041.
  • Wang D, Mo J, Zhou G, Xu L, Liu Y, Gwak J. 2020. An efficient mixture of deep and machine learning models for covid-19 diagnosis in chest x-ray images. PloS one. 15(11):e0242535. doi:10.1371/journal.pone.0242535.
  • Wang Z, Xiao Y, Li Y, Zhang J, Lu F, Hou M, Liu X. 2021. Automatically discriminating and localizing covid-19 from community-acquired pneumonia on chest x-rays. Pattern Recognit. 110:107613. doi:10.1016/j.patcog.2020.107613.
  • Weinstock MB, Echenique A, Russell J, Leib A, Miller J, Cohen D, Waite S, Frye A, Illuzzi F. 2020. Chest x-ray findings in 636 ambulatory patients with covid-19 presenting to an urgent care center: a normal chest x-ray is no guarantee. J Urgent Care Med. 14(7):13–18.
  • Wong KK, Fortino G, Abbott D. 2020. Deep learning-based cardiovascular image diagnosis: a promising challenge. Future Gener Comput Syst. 110:802–811. doi:10.1016/j.future.2019.09.047.
  • Xu S, Wu H, Bie R. 2018. Cxnet-m1: anomaly detection on chest x-rays with image-based deep learning. IEEE Access. 7:4466–4477. doi:10.1109/ACCESS.2018.2885997.
  • Yang D, Martinez C, Visuña L, Khandhar H, Bhatt C, Carretero J. 2021. Detection and analysis of covid-19 in medical images using deep learning techniques. Sci Rep. 11(1):1–13.
  • Yan C, Yao J, Li R, Xu Z, Huang J. Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Washington DC USA; 2018.
  • Zebin T, Rezvy S. 2021. Covid-19 detection and disease progression visualization: deep learning on chest x-rays for classification and coarse localization. Appl Intell. 51(2):1010–1021. doi:10.1007/s10489-020-01867-1.
  • Zebin T, Scully PJ, Peek N, Casson AJ, Ozanyan KB. 2019. Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition. IEEE Access. 7:133509–133520. doi:10.1109/ACCESS.2019.2941836.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.