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DIAGNOSTIC ACCURACY OF DEEP NEURAL NETWORKS FOR PNEUMONIA AND COVID-19 DETECTION ON MEDICAL IMAGING: A SYSTEMATIC REVIEW AND META-ANALYSIS

Johnson Bisi Oluwagbemi(1), Racheal Shade Akinbo(2), Ayobami Emmanuel Mesioye(3),


(1) McPherson University.
(2) Federal University of Technology Akure
(3) McPherson University
Corresponding Author

Abstract


Pneumonia and COVID-19 remain leading causes of universal morbidity and mortality, with timely and precise diagnosis essential for effective patient management. This systematic review and meta-analysis assessed the diagnostic accuracy of deep neural networks in detecting pneumonia and COVID-19 across main medical imaging modalities. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore and Cochrane Library identified 80 eligible studies published between 2017 and 2025. Included studies used chest X-ray (CXR), computed tomography (CT) and lung ultrasound (LUS) images analyzed through convolutional neural networks, transformer-based and hybrid deep models. Pooled diagnostic performance was synthesized using a bivariate random-effects model and hierarchical summary receiver operating characteristic analysis. Overall pooled sensitivity and specificity were 0.88 (95% CI: 0.84-0.91) and 0.90 (95% CI: 0.86-0.92), respectively, with an area under the curve of 0.93, indicating high discriminative capability. Subgroup analyses revealed CT-based models outperformed CXR and LUS, while transformer architectures marginally exceeded CNNs. In addition, external validation studies steadily reported lower accuracy than internal validations, reflecting limited model generalizability. Risk of bias assessment using QUADAS-2 emphasized concerns related to patient selection, data leakage and non-standardized reference criteria. Despite moderate heterogeneity (I² = 39-52%) and potential publication bias, findings confirm the substantial potential of DNNs as decision-support tools for fast, scalable and reliable respiratory disease diagnosis. However, broader clinical adoption demands multicenter validation, transparency and adherence to ethical AI standards. This study provides evidence-based insights into the current performance and translational readiness of AI-driven diagnostic imaging for pneumonia and COVID-19.

Keywords


Deep Neural Networks, Pneumonia, COVID-19, Diagnostic Accuracy, Medical Imaging, Meta-Analysis

References


Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2020). Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence, 51(3), 854–864.

Adeyemi, T., & Okonkwo, C. (2025). AI-driven radiology in sub-Saharan Africa: Bridging the diagnostic gap. African Journal of Medical Imaging, 12(1), 45–58.

Agarwal, A., Singh, B., Kaur, C., & Sharma, D. (2019). Deep learning for detection of pneumonia in pediatric chest radiographs: A retrospective study. Radiology, 290(2), 514–522.

Ahmad, I., Merla, A., Ali, F., Shah, B., AlZubi, A. A., & AlZubi, M. A. (2023). A deep transfer learning approach for COVID-19 detection and exploring a sense of belonging with diabetes. Frontiers in Public Health, 11, 1308404. https://doi.org/10.3389/fpubh.2023.1308404

Ahmed, S., & Khan, M. (2025). Open-source AI in healthcare: Opportunities and challenges. Journal of Digital Health, 7(2), 101–115.

Akinbo, R. S., Olabode, O., Daramola, O., & Ibam, E. (2025). Comparative analysis of machine learning models on the classification of pneumonia disease using chest X-ray images. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 17(2), 23–30. https://doi.org/10.54554/jtec.2025.17.02.003

Akinbo, R. S., Olabode, O., Daramola, O. A., & Ibam, E. O. (2024). Pneumonia disease classification models with ensemble transfer learning approach. International Journal of Computer Applications, 186(32), 59–67. https://doi.org/10.5120/ijca2024923867

Al-Ayyoub, M., Alsmadi, I., & Aljarah, I. (2025). Deep learning in medical imaging: A Jordanian perspective. International Journal of Computer Applications in Medicine, 18(3), 67–78.

Alsharif, M. H., & Alsharif, A. (2025). AI-based diagnostic systems in low-resource settings: A review. Global Health Technology, 9(1), 33–47.

Alvarez, J. (2020). Artificial intelligence in radiology: A paradigm shift. Radiology Today, 21(4), 22–28.

Apostolopoulos, I. D., & Mpesiana, T. A. (2020). COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635–640.

Bashar, M. (2019). Transformer networks for medical image classification. IEEE Transactions on Medical Imaging, 38(11), 2653–2662.

Bello, R., & Yusuf, A. (2024). AI-assisted triage in Nigerian emergency departments. West African Journal of Emergency Medicine, 6(2), 89–97.

Bushberg, J. T., Seibert, J. A., Leidholdt, E. M., & Boone, J. M. (2018). The essential physics of medical imaging (4th ed.). Wolters Kluwer.

Centers for Disease Control and Prevention. (2020). Pneumonia: Causes and prevention. Centers for Disease Control and Prevention. https://www.cdc.gov/pneumonia

Centers for Disease Control and Prevention (CDC). (2020). Pneumonia: Causes and prevention. Retrieved from https://www.cdc.gov/pneumonia

Cheng, J. (2020). Deep learning in COVID-19 diagnosis: A review. Journal of Biomedical Informatics, 108, 103512.

Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z., Islam, K. R., Khan, M. S., Iqbal, A., Al Emadi, N., Bin Ibne Reaz, M., & Islam, M. T. (2020). Can AI help in screening viral and COVID-19 pneumonia? IEEE Access, 8, 132665–132676. https://doi.org/10.1109/ACCESS.2020.3010287

Deeks, J. J., Macaskill, P., Irwig, L., & Bossuyt, P. M. (2005). The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. Journal of Clinical Epidemiology, 58(9), 882–893.

Erthal, L., Fraga, J. S., Santos, A. C., & Appenzeller, S. (2018). Deep learning in medical imaging: A Brazilian perspective. Radiologia Brasileira, 51(4), 245–252. https://doi.org/10.1590/0100-3984.2017.0088

Fernandes, R., & Silva, M. (2024). Ethical frameworks for AI in healthcare. Journal of Medical Ethics and Technology, 15(1), 23–35.

Ghebreyesus, T. A. (2020). WHO Director-General’s opening remarks at the media briefing on COVID-19. World Health Organization. Retrieved from https://www.who.int

Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing (4th ed.). Pearson.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Gupta, R. (2021). CNN-based pneumonia detection in chest X-rays. International Journal of Computer Vision in Medicine, 9(3), 112–120.

Haddad, D., O’Quinn, M., & Moore, J. (2019). Radiology workflows and AI integration. Journal of Clinical Imaging Science, 9(1), 45–52.

Szepesi, P., & Szilágyi, L. (2022). Detection of pneumonia using convolutional neural networks and deep learning. Biocybernetics and Biomedical Engineering, 42(3), 1012–1022. https://doi.org/10.1016/j.bbe.2022.08.001 (ScienceDirect)

Hernández-Boussard, T., Bozkurt, S., Ioannidis, J. P. A., & Shah, N. H. (2020). Transparent reporting of AI in medical research. Nature Medicine, 26(10), 1364–1366. https://doi.org/10.1038/s41591-020-1041-1

Saw, S. N., Yan, Y. Y., & Ng, K. H. (2024). Current status and future directions of explainable artificial intelligence in medical imaging. European Journal of Radiology, 183, 111884. https://doi.org/10.1016/j.ejrad.2024.111884

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.

International Labour Organization. (2021). COVID-19 and the world of work. Retrieved from https://www.ilo.org

International Monetary Fund. (2021). World economic outlook: Pandemic impact. Retrieved from https://www.imf.org

Ibam, E. O., Oluwagbemi, J. B., & Oladapo, K. A. (2025). Edge-cloud enabled multimodal framework for real-time pneumonia detection using wearable sensors. Information Sciences and Technological Innovations, 2(2), 88–101.

Jain, A. (2020). AI in pandemic response: Lessons from COVID-19. Health Informatics Journal, 26(4), 289–298.

Adamu, V. E., & Eneojo, N. I. F. (2024, August 22). Building an AI-era healthcare business. Orapuh Journal, 5(4), e1139. https://doi.org/10.4314/orapj.v5i4.39

Huang, DM., Huang, J., Qiao, K. et al. Deep learning-based lung sound analysis for intelligent stethoscope. Military Med Res 10, 44 (2023). https://doi.org/10.1186/s40779-023-00479-3

Kareem, M., Liu, Y., & Sant, G. (2022). AI in respiratory diagnostics: A narrative review. Journal of Pulmonary Medicine, 14(1), 34–42.

Kenny, C. (2020). Machine learning for pneumonia detection: A comparative study. Computers in Biology and Medicine, 123, 103865.

Kim, J., & Park, H. (2023). Multimodal AI for respiratory disease diagnosis. Journal of Medical Imaging and Health Informatics, 13(2), 78–90.

LeCun, Y., Bengio, Y., & Hinton, G. (2017). Convolutional networks for medical image analysis. Annual Review of Biomedical Engineering, 19, 327–351.

Liu J, Qi J, Chen W, Wu Y, Nian Y. Deep Learning for Detecting COVID-19 Using Medical Images. Bioengineering (Basel). 2022 Dec 22;10(1):19. doi: 10.3390/bioengineering10010019. PMID: 36671590; PMCID: PMC9854504.

Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., Ashrafian, H., Beam, A. L., Chan, A. W., Collins, G. S., Deeks, J. J., El Zarrad, M. K., Espinoza, C., Esteva, A., Faes, L., Ferrante di Ruffano, L., Fletcher, J., Golub, R., Harvey, H., Haug, C., … Yau, C. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nature Medicine, 26(9), 1364–1374. https://doi.org/10.1038/s41591-020-1034-x

Lu, L., & Liang, M. (2025). Deep learning-driven medical image analysis for computational material science applications. Frontiers in Materials, 12, 1583615. https://doi.org/10.3389/fmats.2025.1583615

Mandell, G. L., Bennett, J. E., & Dolin, R. (2017). Mandell, Douglas, and Bennett's principles and practice of infectious diseases (8th ed.). Elsevier.

McInnes, M. D. F., et al. (2018). PRISMA-DTA: Reporting guideline for diagnostic test accuracy systematic reviews. BMJ, 358, j4008.

Naqvi, S., & Choudhry, M. (2020). SVM-based pneumonia detection in chest radiographs. Journal of Medical Imaging and Diagnostics, 8(2), 55–63.

Oliveira, F., & Costa, R. (2023). AI in radiology: Reducing workload and improving outcomes. European Journal of Radiology, 154, 110412.

Pandey, A., Pallavi, & Pandey, R. (2019). Deep learning for pneumonia detection: A comparative study. International Journal of Biomedical Engineering, 7(1), 23–30.

Patel, N., & Mehta, S. (2023). Meta-analysis of AI in respiratory diagnostics. Journal of Evidence-Based Medicine, 16(1), 12–25.

Pew Research Center. (2021). Public health and economic impact of COVID-19. Retrieved from https://www.pewresearch.org

Puneet, S. (2021). COVID-19 diagnosis: Imaging and AI perspectives. Indian Journal of Radiology and Imaging, 31(2), 145–150.

Rahman, M., & Hossain, M. (2024). AI-assisted triage in Bangladesh: A pilot study. South Asian Journal of Emergency Medicine, 5(1), 33–41.

Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Patel, K., & Ng, A. Y. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning (Preprint). arXiv. https://arxiv.org/abs/1711.05225

Rohani, M. M., Sharifi, S., & Durson, S. (2025). Deep learning in medical imaging for disease diagnosis. World Journal of Advanced Research and Reviews, 25(2), 2522–2526. https://doi.org/10.30574/wjarr.2025.25.2.0558

Okeh, U. M., & Okoro, N. (2025). Measure of association dependent on sensitivity and specificity of diagnostic screening tests in a study population. The Nigerian Health Journal, 24, Article 776. https://tnhjph.com/index.php/tnhj/article/view/776/716

Vyskočil, J., & Picek, L. (2021). Improving web user interface element detection using Faster R-CNN. In CLEF 2021-Conference and Labs of the Evaluation Forum (Working Notes, Vol. 2936). CEUR Workshop Proceedings. Retrieved from https://ceur-ws.org/Vol-2936/paper-117.pdf

Sai Prasad, R., Nair, A., Thomas, S., & Iqbal, M. (2025). COVID-19 detection using hybrid deep learning models: A multicenter study. Journal of Medical Imaging and Diagnostics, 17(1), 55–68.

Saleem, M., Khan, M. A., Raza, M., & Zafar, A. (2022). Explainable AI for COVID-19 diagnosis using chest X-rays. Computers in Biology and Medicine, 144, 105372.

Salehi, S., Abedi, A., Balakrishnan, S., & Gholamrezanezhad, A. (2020). Coronavirus disease 2019 (COVID-19): A systematic review of imaging findings in 919 patients. AJR American Journal of Roentgenology, 215(1), 87–93.

Sanjay, R. (2022). Pneumonia in children under five: A global health perspective. Global Pediatric Health, 9, 2333794X221089456.

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.

Schünemann, H. J., Mustafa, R. A., Brozek, J., Santesso, N., Alonso-Coello, P., Guyatt, G. H., ... & Oxman, A. D. (2020). GRADE guidelines: 22. The GRADE approach for tests and strategies in diagnostic test accuracy. Journal of Clinical Epidemiology, 122, 129–141.

Shah, A., & Shah, R. (2022). AI in respiratory diagnostics: A review of current applications. Journal of Pulmonary Research, 10(2), 101–110.

Schumacher, M., Rücker, G., & Schwarzer, G. (2021). mada: Meta-analysis of diagnostic accuracy. R package version 0.5.10.

Singh, R., & Kumar, A. (2025). Multimodal AI for pneumonia detection: Integrating imaging and clinical data. Journal of Biomedical Informatics, 132, 104234.

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-ResNet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 4278–4284.

Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2020). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299–1312. https://doi.org/10.1109/TMI.2016.2535302

Talo, M. (2019). Automated pneumonia detection using deep learning approaches with chest X-ray images. Computers in Biology and Medicine, 118, 103738.

Thakral, R., Gupta, A., & Sharma, P. (2019). Deep learning for pneumonia detection: A comparative study. International Journal of Computer Applications, 178(7), 1–6.

Torres, J., & Martinez, L. (2023). Prospective validation of AI models in respiratory diagnostics: A multicenter study. European Respiratory Journal, 61(2), 2201456.

Khan, L., Khan, M., Rzayev, N., Kour, M., Rana, T., Ahmad, M., & Lac, J. (2025). MetaLLMReporter: An R Shiny app integrating meta analysis execution with LLM assisted reporting [Software Tool Article]. F1000Research, 14, 724. https://doi.org/10.12688/f1000research.166080.1

Subramanian, N., Elharrouss, O., Al-Maadeed, S., & Chowdhury, M. (2022). A review of deep learning-based detection methods for COVID-19. Computers in Biology and Medicine, 143, 105233. https://doi.org/10.1016/j.compbiomed.2022.105233

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases (arXiv:1705.02315). arXiv. https://doi.org/10.48550/arXiv.1705.02315

Wang, L., Lin, Z.Q. & Wong, A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10, 19549 (2020). https://doi.org/10.1038/s41598-020-76550-z

Wikipedia. (2021). Medical imaging. Retrieved from https://en.wikipedia.org/wiki/Medical_imaging

Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., ... & Bossuyt, P. M. (2011). QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155(8), 529–536.

World Health Organization. (2020). WHO coronavirus dashboard. Retrieved from https://covid19.who.int

Wu, J., Liu, Y., & Liu, Z. (2019). Deep learning in radiology: Current status and future directions. Radiology, 290(1), 7–15.

Yang, G., Zhang, J., Liu, J., Sun, M., & Qian, D. (2020). QUADAS-AI: A tailored quality assessment tool for AI diagnostic studies. Artificial Intelligence in Medicine, 107, 101894.

Zang, Y., Zhang, Y., & Wang, J. (2018). Transformer networks for medical image classification. IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5345–5355.

Zhang, Y., & Liu, H. (2024). Explainable AI in medical imaging: A systematic review. Journal of Digital Health, 8(1), 23–39.


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DOI: 10.56327/ijiscs.v9i3.1857

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