Neural Networks for the Diagnosis of Covid-19 in Chest X-ray Images: A Systematic Review and Meta-Analysis

Daniel Cristobal Andrade-Girón; William Joel Marín-Rodriguez; Flor de María Lioo-Jordán; Gladis Jane Villanueva-Cadenas; Flor de María Garivay-Torres de Salinas

doi:10.4108/eetpht.9.4212

Authors

Daniel Cristobal Andrade-Girón Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0002-9746-3583
William Joel Marín-Rodriguez Universidad Tecnológica del Perú https://orcid.org/0000-0002-0861-9663
Flor de María Lioo-Jordán Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0001-6659-8621
Gladis Jane Villanueva-Cadenas Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0001-6659-8621
Flor de María Garivay-Torres de Salinas Universidad Nacional José Faustino Sánchez Carrión https://orcid.org/0000-0001-6659-8621

DOI:

https://doi.org/10.4108/eetpht.9.4212

Keywords:

prediction, COVID-19, model VGG16, deep learning

Abstract

Introduction: The COVID-19 pandemic has triggered a global crisis with significant repercussions in terms of mortality and an ever-increasing demand for urgent medical care, particularly in emergency care settings. This demand arises mainly from the prevailing need to carry out real-time diagnoses and provide immediate care to patients at high risk of serious complications. With the purpose of addressing this problem in a rigorous manner, we have carried out a systematic review focused on evaluating the effectiveness of models based on neural networks for the diagnosis of COVID-19 from chest x-ray images.

Methods: This review has been carried out through an exhaustive search in various renowned electronic bibliographic databases, such as Scopus, IEEE Xplore, PubMed and ScienceDirect. The search period has been extended until September 2023, culminating in the identification of a total of 1,250 relevant articles.

Results: The culminating phase of our review involved the inclusion of 37 studies that met rigorously established selection criteria. These studies have been the subject of a thorough analysis, where various performance metrics such as accuracy/precision, sensitivity/recall, specificity and the F1 value (F1-score) have been evaluated.

Conclusions: Our results reveal that the VGG16 (Visual Geometry Group 16) model, based on neural networks, has emerged as the most widely adopted, manifesting itself in 13.04% of the total models analyzed and in 16.21% of the models supported by the 37 studies. selected. Notably, this algorithm has exhibited an impressive accuracy of over 99% in predicting the diagnosis of patients with suspected COVID-19.

Downloads

Download data is not yet available.

References

Afif A, Hafsa NE, Ali MA, Alhumam A, Alsalman S. An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images. Symmetry. 2021;13(1):113. Disponible en: https://doi.org/10.3390/sym13010113.

Ahmed AH, AI-Hamadani MN, Satam IA. Prediction of COVID-19 disease severity using machine learning techniques. Bull Electr Eng Informatics. 2022;11(2):1069-1074. doi:10.11591/eei.v11i2.3272.

Ahuja S, Panigrahi BK, Dey N, Rajinikanth T. Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell. 2021;51:571-585. doi:10.1007/s10489-020-01626-w.

Akter S, Mehedi Shamrat FJ, Chakraborty S, Karim A, Azam S. COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images. Biology. 2021;10(11):1174. doi:10.3390/biology10111174.

Alexander PA. Methodological guidance paper: The art and science of quality systematic reviews. Rev Educ Res. 2020;90(1):6-23. Disponible en: https://doi.org/10.3102/0034654319854352.

Andrade-Girón D, Carreño-Cisneros E, Mejía-Dominguez C, Marín-Rodriguez W, Villarreal-Torres H. Comparación de Algoritmos Machine Learning para la Predicción de Pacientes con Sospecha de COVID-19. Salud, Ciencia y Tecnología. 2023;3:336-336. Disponible en: https://doi.org/10.56294/saludcyt2023336.

Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43:635-640. doi:10.1007/s13246-020-00865-4.

Atitallah SB, Driss M, Boulila W, Ben Ghézala H. Randomly initialized convolutional neural network for the recognition of COVID-19 using X-ray images. Natl Cent Biotechnol Info. 2021;32(1):55-73. doi:10.1002/ima.22654.

Biswas S, Chatterjee S, Majee A, Sen S, Schwenker F, Sarkar R. Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models. Appl Sci. 2021;11(15):7004. Disponible en: https://doi.org/10.3390/app11157004.

Brereton KB, Budgen OP, Turner D, Bailey J, Linkmen S. Systematic reviews of the software engineering literature: a systematic review of the literature. Inf Technol Comput Softw. 2009;51(1):7-15. Disponible en: https://doi.org/10.1016/j.infsof.2008.09.009.

Buvana M, Muthumayil K, Kumar SS, Nebhen J, Alshamrani SS, Ali I. Deep optimal VGG16 based COVID-19 diagnosis model. Computers, Materials and Continua. 2022;70(1):43-58. Disponible en: https://doi.org/10.32604/cmc.2022.019331.

Buvana N, Muthumayil K. Predicción del paciente con COVID-19 utilizando un algoritmo de aprendizaje automático supervisado. Santos Malasia. 2021;50(8):2479-2497. doi:http://doi.org/10.17576/jsm-2021-5008-28.

Chakraborty S, Murali B, Mitra AK. An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images. International Journal of Environmental Research and Public Health. 2022;19(4). Disponible en: https://doi.org/10.3390/ijerph19042013.

Chandra T, Verma K, Singh B, Jain D, Netam S. Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert Systems With Applications. 2021;165:113909. Disponible en: https://doi.org/10.1016/j.eswa.2020.113909.

Comito C, Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artificial Intelligence in Medicine. 2022;128:102286. Disponible en: https://doi.org/10.1016/j.artmed.2022.102286.

Cortés ME. La pandemia de COVID-19: importancia de estar alerta ante las zoonosis. Revista de la Facultad de Medicina Humana. 2021;21(1):151-156. doi:http://dx.doi.org/10.25176/rfmh.v21i1.3451.

Dunlop C, Howe A, Li D, Allen LN. The coronavirus outbreak: the central role of primary care in emergency preparedness and response. BJGP open. 2020;4(1). doi:https://doi.org/10.3399/bjgpopen20X101041.

Fuentes Marmolejo MD, Medina Parra WD. Diseño de un modelo predictivo-asistencial de pacientes infectados por Covid-19, mediante un modelo supervisado de Machine Learning basado en criterios de derivación hospitalaria o ambulatoria. Universidad de Guayaquil.

Gao Y, Cai G-Y, Fang W, Li H-Y, Wang S-Y, Chen L, et al. Machine learning based early warning system enables accurate mortality risk prediction for COVID-19. Nature Communications. 2020;11(1):5033. Disponible en: https://doi.org/10.1038/s41467-020-18684-2.

Garcell HG, Valdes AG, Alvarez LG. COVID-19 y el problema de los tiempos en las estrategias de control. Revista Habanera de Ciencias Médicas. 2020;19:1-7. Disponible en: https://revhabanera.sld.cu/index.php/rhab/article/view/3318.

Garcia-Alamino JM. Aspectos epidemiológicos, clínica y mecanismos de control de la pandemia por Sars-Cov-2: situación en España. Enfermería clínica. 2021;S4-S11. Disponible en: https://doi.org/10.1016/j.enfcli.2020.05.001.

Gayathri JL, Abraham B, Sujarani MS, Nair MS. A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network. Computers in Biology and Medicine. 2021;141:105134. Disponible en: https://doi.org/10.1016/j.compbiomed.2021.105134.

Goel T, Murugan R, Mirjalili S, Kumar Chakrabartty D. OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Applied Intelligence. 2021;51:1351–1366. doi:10.1007/s10489-020-01904-z.

Guan WJ, Ni ZY, Hu Y, Lian W H, Ou C Q, He J X, et al. Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine. 2020;382(18):1708-1720. doi:10.1056/NEJMoa2002032.

Gupta V, Jain N, Sachdera J, Gupta M, Mohan S, Yazid B, Ahmadian A. Improved COVID-19 detection with chest x-ray images using deep learning. Multimedia Tools and Applications. 2022;81:37657-37680. doi:10.1007/s11042-022-13509-4.

Hu H, Peng R, Tai Y W, Tang C K. Network trimming: A data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv. 2016;12:1607.03250. Disponible en: https://doi.org/10.48550/arXiv.1607.03250.

Iliev N, Trivedi AR. Low latency CMOS hardware acceleration for fully connected layers in deep neural networks. arXiv preprint arXiv. 2020;25:2011.12839. Disponible en: https://doi.org/10.48550/arXiv.2011.12839.

Ismael AM, Şengür A. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications. 2021;164:114054. Disponible en: https://doi.org/10.1016/j.eswa.2020.114054.

Jin W, Dong S, Dong C, Ye X. Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph. Computers in Biology and Medicine. 2021;104252. Disponible en: https://doi.org/10.1016/j.compbiomed.2021.104252.

Kaur P, Harnal S, Tiwari R, Alharithi FS, Almulihi AH, Delgado Noya I, Goyal N. A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images. International Journal of Environmental Research and Public Health. 2021;18(22):12191. Disponible en: https://doi.org/10.3390/ijerph182212191.

Khan AI, Shah JL, Bhat MM. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine. 2020;196:105581. Disponible en: https://doi.org/10.1016/j.cmpb.2020.105581.

Kogilavani SV, Prabhu J, Sandhiya R, Kumar MS, Subramaniam U, Karthick A, Sheik Imam SB. COVID-19 detection based on lung CT scan using deep learning techniques. Computational and Mathematical Methods in Medicine. 2022. Disponible en: https://doi.org/10.1155/2022/7672196.

Kong L, Cheng J. Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion. Biomedical Signal Processing and Control. 2022;77:103772. Disponible en: https://doi.org/10.1016/j.bspc.2022.103772.

Kumari S, Ranjith E, Gujjar A, Narasimman S, Zeelani HA. Comparative analysis of deep learning models for COVID-19 detection. Global Transitions Proceedings. 2021;2(2):559-565. Disponible en: https://doi.org/10.1016/j.gltp.2021.08.030.

Li X, Tan W, Liu P, Zhou Q, Yang J. Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning. Hindawi Journal of Healthcare Engineering. 2021. Disponible en: https://doi.org/10.1155/2021/5528441.

Madaan V, Roy A, Gupta C, Agrawal P, Sharma A, Bologa C, Prodan R. XCOVNet: Chest X ray Image Classification for COVID 19 Early Detection Using Convolutional Neural Networks. New Generation Computing. 2021;39:583–597. Disponible en: https://doi.org/10.1007/s00354-021-00121-7.

Manosalvas ZM, Zamora SS. Validación de la escala News 2 para predecir deterioro clínico en pacientes adultos mayores hospitalizados con neumonía por SARS CoV-2/COVID-19. Hospital de Especialidades Eugenio Espejo. Disponible en: http://repositorio.puce.edu.ec:80/handle/22000/20775.

Marques G, Agarwal D, De la Torre Díez I. Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network. Applied Soft Computing Journal. 2020;96:106691. Disponible en: https://doi.org/10.1016/j.asoc.2020.106691.

Nayak SR, Nayak DR, Sinha U, Arora V, Pachori RB. An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images. Diagnostics. 2022;13(1):131. Disponible en: https://doi.org/10.3390/diagnostics13010131.

Nayak SR, Nayak J, Sinha U, Arora V, Ghohs U, Satapathy SC. An Automated Lightweight Deep Neural Network for Diagnosis of COVID 19 from Chest X ray Images. Arabian Journal for Science and Engineering. 2021;48:1085–11102. doi:10.1007/s13369-021-05956-2.

Oh Y, Park S, Chul Ye J. Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets. IEEE TRANSACTIONS ON MEDICAL IMAGING. 2020;39(8):2688-2700. doi:10.1109/TMI.2020.2993291.

Ozturk T, Talo M, Azra Yildiri E, Baran Baloglu U, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine. 2020;121:103792. Disponible en: https://doi.org/10.1016/j.compbiomed.2020.103792.

Ozturk T, Talo M, Yildirim EA, Baloglu U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine. 2020;121:103792. Disponible en: https://doi.org/10.1016/j.compbiomed.2020.103792.

Panwar H, Gupta PK, Siddiqui MK, Morales-Menendez R, Singh V. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons & Fractals. 2020;138:109944. Disponible en: https://doi.org/10.1016/j.chaos.2020.109944.

Perumal M, Nayak A, Praneetha Sree R, Srinivas M. INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network. ISA Transactions. 2022;124:82-89. Disponible en: https://doi.org/10.1016/j.isatra.2022.02.033.

Pigott TD, Polanin JR. Methodological guidance paper: High-quality meta-analysis in a systematic review. Review of Educational Research. 2020;90(1):24-46. doi:10.3102/0034654319877153.

Prakash KB, Imambi SS, Ismail M, Kumar TP, Pawan YN. Analysis, prediction and evaluation of covid-19 datasets using machine learning algorithms. International Journal. 2020;8(5):2199-2204. Disponible en: https://doi.org/10.30534/ijeter/2020/117852020.

Rehman A, Naz S, Razzak MI, Akram F, Imran M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits, Systems, and Signal Processing. 2019;39:757-775. Disponible en: https://doi.org/10.1007/s00034-019-01246-3.

Sadik F, Dastider AG, Subah MR, Mahmud T, Fattah SA. A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images. Computers in biology and medicine. 2022;149:105806. Disponible en: https://doi.org/10.1016/j.compbiomed.2022.105806.

Sahinbas K, Catak FO. Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images. In Data science for COVID-19. 2021;451-466. Disponible en: https://doi.org/10.1016/B978-0-12-824536-1.00003-4.

Sánchez MJ. Cómo realizar una revisión sistemática y un meta-análisis. Aula Abierta. 2010;38(2):53-64. Disponible en: http://hdl.handle.net/11162/5126.

Sánchez-Duque JA, Arce-Villalobos LR, Rodríguez-Morales AJ. Enfermedad por coronavirus 2019 (COVID-19) en América Latina: papel de la atención primaria en la preparación y respuesta. Atención primaria. 2020;52(6):369-372. doi:10.1016/j.aprim.2020.04.001.

Sanket S, Raja Sarobin V, Jani Anbaras L, Thakor J, Singh U, Narayanan S. Detection of novel coronavirus from chest X rays using deep convolutional neural networks. Multimedia Tools and Applications. 2022;81:22263–22288. Disponible en: https://doi.org/10.1007/s11042-021-11257-5.

Schwarzer G, Carpenter JR, Rücker G. Meta-analysis with R. Springer. 2015. Disponible en: https://link.springer.com/book/10.1007/978-3-319-21416-0.

Serrano SS, Navarro IP, González MD. ¿Cómo hacer una revisión sistemática siguiendo el protocolo PRISMA?: Usos y estrategias fundamentales para su aplicación en el ámbito educativo a través de un caso práctico. Bordón: Revista de pedagogía. 2022;74(3):51-66. Disponible en: file:///C:/Users/V/Downloads/Dialnet-ComoHacerUnaRevisionSistematicaSiguiendoElProtocol-8583045.pdf.

Shorfuzzaman M. IoT-enabled stacked ensemble of deep neural networks for the diagnosis of COVID-19 using chest CT scans. Computing. 2023;105:887–908. doi:10.1007/s00607-021-00971-5.

Silveira EC. Prediction of COVID-19 from hemogram results and age using machine learning. Frontiers in Health Informatics. 2020;9(1):39. Disponible en: https://doi.org/10.30699/fhi.v9i1.234.

Sing Punn N, Agarwal S. Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Applied Intelligence. 2021;51:2689–2702. doi:10.1007/s10489-020-01900-3.

Sun J, Peng PP, Chaosheng T, Wang S-H, Zhang D-Y. TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model. Computers in Biology and Medicine. 2022;146:105531. Disponible en: https://doi.org/10.1016/j.compbiomed.2022.105531.

Toraman S, Alakus TB, Turkoglu I. Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks. Chaos, Solitons & Fractals. 2020;140:110122. Disponible en: https://doi.org/10.1016/j.chaos.2020.110122.

Ucara F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses. 2020;140:109761. Disponible en: https://doi.org/10.1016/j.mehy.2020.109761.

Verma SS, Prasad A, Kumar A. CovXmlc: High performance COVID-19 detection on X-ray images using Multi-Model classification. Biomedical signal processing and control. 2022;71:103272. Disponible en: https://doi.org/10.1016/j.bspc.2021.103272.

Wang W, Jiang Y, Wang X, Zhang P, Li J. Detecting COVID-19 patients via MLES-Net deep learning models from X-Ray images. BMC Medical Imaging. 2022;22:135. Disponible en: https://doi.org/10.1186/s12880-022-00861-y.

Yang D, Martinez C, Visuña L, Khandhar H, Bhatt C, Carretero J. Detection and analysis of COVID-19 in medical images using deep learning techniques. Scientific Reports. 2021;11(1):19638. Disponible en: https://doi.org/10.1038/s41598-021-99015-3.