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Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the medical imaging data and pose risks of data breaches and exploitation; however, federated learning is a decentralized architecture which significantly reduces data privacy concerns.

Methods

The federated learning works in a distributed architecture by sending a global model to clients rather than sending the data to the model. The proposed federated deep learning-based FedPneu computer-aided diagnosis model has been implemented in 2, 3, 4, and 5 clients architecture for early pneumonia detection using X-ray images. The key parameters configuration include batch size, learning rate, optimizer, decay, momentum, epochs, rounds, and random-split as 32, 0.0001, SGD, 0.000001, 0.9, 10, 100, and 42, respectively.

Results

The results of the proposed federated deep learning-based FedPneu model have been provided in terms of round-wise accuracy, loss, and computational time. The highest accuracy of 85.632% has been achieved with 2-clients federated deep learning architecture, whereas, 3, 4, and 5 clients architecture achieved 85.536%, 76.112%, and 74.123% accuracies, respectively.

Conclusion

In the proposed privacy-protected federated deep learning-based FedPneu model, the two-client architecture has been resulted as the most optimal framework for pneumonia detection among 3-clients, 4-clients, and 5-clients architecture. The model works in a collaborative and privacy-protected framework with a multi-silo dataset which could be highly beneficial for healthcare departments to maintain patient’s data privacy with improved prediction outcomes.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-01-02
2025-11-02

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References

  1. GuptaK. GargA. KukrejaV. GuptaD. Rice Diseases Multi-Classification: An Image Resizing Deep Learning Approach.2021 International Conference on Decision Aid Sciences and Application (DASA)2021170510.1109/DASA53625.2021.9682298
     [Google Scholar]
  2. SharmaS GuleriaK. A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images.Multimed Tools Appl202383241012415110.1007/s11042‑023‑16419‑1
     [Google Scholar]
  3. KumarS. TiwariS. TewatiaH. JainA. PopliJ. DynaTrack: Machine learning aided variable speed limit system.Procedia Comput. Sci.2023218839210.1016/j.procs.2022.12.404
     [Google Scholar]
  4. SharmaS. GuleriaK. A deep learning based model for the detection of pneumonia from chest x-ray images using VGG-16 and neural networks.Procedia Comput. Sci.202321835736610.1016/j.procs.2023.01.018
     [Google Scholar]
  5. SharmaS. GuleriaK. Pneumonia Detection from Chest X-ray Images using Transfer Learning.2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)202210.1109/ICRITO56286.2022.9964588
     [Google Scholar]
  6. GuleriaK. SharmaS. KumarS. TiwariS. Early prediction of hypothyroidism and multiclass classification using predictive machine learning and deep learning.Meas. Sensors20222410048210.1016/j.measen.2022.100482
     [Google Scholar]
  7. MiottoR. WangF. WangS. JiangX. DudleyJ.T. Deep learning for healthcare: review, opportunities and challenges.Brief. Bioinform.20181961236124610.1093/bib/bbx04428481991
     [Google Scholar]
  8. PatelY.S. BanerjeeS. MisraR. DasS.K. Low-Latency Energy-Efficient Cyber-Physical Disaster System Using Edge Deep Learning.Proceedings of the 21st International Conference on Distributed Computing and Networking (ICDCN '20). Association for Computing Machinery, New York, NY, USA20201610.1145/3369740.3372752
     [Google Scholar]
  9. KaushalC. BhatS. KoundalD. SinglaA. Recent trends in Computer Assisted Diagnosis (CAD) system for breast cancer diagnosis using histopathological images.IRBM201940421122710.1016/j.irbm.2019.06.001
     [Google Scholar]
  10. SharmaR. KukrejaV. Amalgamated convolutional long term network (CLTN) model for Lemon Citrus Canker Disease Multi-classification.2022 International Conference on Decision Aid Sciences and Applications (DASA)2022326910.1109/DASA54658.2022.9765005
     [Google Scholar]
  11. HaoJ. ZhangL. ZhaoY. Grouped federated learning for time-sensitive tasks in industrial IoTs.Peer-to-Peer Netw. Appl.20241781983310.1007/s12083‑023‑01616‑4
     [Google Scholar]
  12. KrizhevskyA. SutskeverI. HintonG.E. ImageNet classification with deep convolutional neural networks.Commun. ACM2017606849010.1145/3065386
     [Google Scholar]
  13. ZhouW. JiaZ. FengC. LuH. LyuF. LiL. Towards driver distraction detection: a privacy-preserving federated learning approach.Peer-to-Peer Netw. Appl.202417111510.1007/s12083‑024‑01639‑5
     [Google Scholar]
  14. EstevaA. KuprelB. NovoaR.A. KoJ. SwetterS.M. BlauH.M. ThrunS. Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks.Nature2017546766068610.1038/nature2298528658222
     [Google Scholar]
  15. ShabutA.M. Hoque TaniaM. LwinK.T. EvansB.A. YusofN.A. Abu-HassanK.J. HossainM.A. An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time.Expert Syst. Appl.2018114657710.1016/j.eswa.2018.07.014
     [Google Scholar]
  16. TiwariS. KumarS. GuleriaK. Outbreak trends of coronavirus disease–2019 in India: A prediction.Disaster Med. Public Health Prep.2020145e33e3810.1017/dmp.2020.11532317044
     [Google Scholar]
  17. PeiX. RenY. TangY. WangY. ZhangL. WeiJ. ZhaoD. MFDiff: multiscale feature diffusion model for segmentation of 3D intracranial aneurysm from CT images.Pattern Anal. Appl.20242726610.1007/s10044‑024‑01266‑z
     [Google Scholar]
  18. ZhaoC. XiangS. WangY. CaiZ. ShenJ. ZhouS. ZhaoD. SuW. GuoS. LiS. Context-aware network fusing transformer and V-Net for semi-supervised segmentation of 3D left atrium.Expert Syst. Appl.202321411910511910510.1016/j.eswa.2022.119105
     [Google Scholar]
  19. WHO Coronavirus (COVID-19) dashboard.Available from: https://covid19.who.int/?mapFilter=deaths
  20. RanaM.S. NibaliA. HeZ. Selection of object detections using overlap map predictions.Neural Comput. Appl.20223421186111862710.1007/s00521‑022‑07469‑x
     [Google Scholar]
  21. McMahanB. MooreE. RamageD. HampsonS. Communication-Efficient Learning of Deep Networks from Decentralized Data.Proceedings of the 20th International Conference on Artificial Intelligence and Statistics2017123782
     [Google Scholar]
  22. LiuY. LiH. HaoM. SVFGNN: A privacy-preserving vertical federated graph neural network model training framework based on split learning.Peer-to-Peer Netw. Appl.202317246260
     [Google Scholar]
  23. TangS. DingY. ZhaoM. WangH. SAKMR: Industrial control anomaly detection based on semi-supervised hybrid deep learning.Peer-to-Peer Netw. Appl.202417612623
     [Google Scholar]
  24. FengS. Vertical federated learning-based feature selection with non-overlapping sample utilization.Expert Syst. Appl.202220811809710.1016/j.eswa.2022.118097
     [Google Scholar]
  25. HuangZ. LiuX. WangR. ZhangM. ZengX. LiuJ. YangY. LiuX. ZhengH. LiangD. HuZ. FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.Appl. Intell.20215152838284910.1007/s10489‑020‑01965‑034764567
     [Google Scholar]
  26. RajpurkarP. IrvinJ. ZhuK. YangB. MehtaH. DuanT. CheXNet: Radiologist-level pneumonia detection on chest x-rays with deep learning.arxiv20172017
     [Google Scholar]
  27. J LG. AbrahamB. M SS. NairM.S. 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.Comput. Biol. Med.202214110513410513410.1016/j.compbiomed.2021.10513434971978
     [Google Scholar]
  28. IbrahimA.U. OzsozM. SerteS. Al-TurjmanF. YakoiP.S. Pneumonia classification using deep learning from chest x-ray images during COVID-19.Cognit. Comput.202111333425044
     [Google Scholar]
  29. RehmanM.U. ShafiqueA. KhanK.H. KhalidS. AlotaibiA.A. AlthobaitiT. RamzanN. AhmadJ. ShahS.A. AbbasiQ.H. Novel privacy preserving non-invasive sensing-based diagnoses of pneumonia disease leveraging deep network Model.Sensors (Basel)202222246110.3390/s2202046135062422
     [Google Scholar]
  30. LiuB. YanB. ZhouY. YangY. ZhangY. Experiments of federated learning for COVID-19 chest x-ray images.arxiv20202020
     [Google Scholar]
  31. AfsharP. HeidarianS. NaderkhaniF. OikonomouA. PlataniotisK.N. MohammadiA. COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images.Pattern Recognit. Lett.202013863864310.1016/j.patrec.2020.09.01032958971
     [Google Scholar]
  32. TsiknakisN. TrivizakisE. VassalouE. PapadakisG. SpandidosD. TsatsakisA. Sánchez-GarcíaJ. López-GonzálezR. PapanikolaouN. KarantanasA. MariasK. Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays.Exp. Ther. Med.202020272773510.3892/etm.2020.879732742318
     [Google Scholar]
  33. BanerjeeS. MisraR. PrasadM. ElmrothE. BhuyanM.H. Multi-diseases Classification from Chest-X-ray: A Federated Deep Learning Approach.AI 2020: Advances in Artificial Intelligence.202031510.1007/978‑3‑030‑64984‑5_1
     [Google Scholar]
  34. ChenY. QinX. WangJ. YuC. GaoW. FedHealth: A federated transfer learning framework for wearable healthcare.IEEE Intell. Syst.2020354839310.1109/MIS.2020.2988604
     [Google Scholar]
  35. KhanS.H. AlamM.G.R. A Federated Learning Approach to Pneumonia Detection.2021 International Conference on Engineering and Emerging Technologies (ICEET)20211610.1109/ICEET53442.2021.9659591
     [Google Scholar]
  36. BalajiN.N.A. NarayananN. ThellyK.J. BabuC. Investigating Federated Learning strategies for Pneumonia Image Classification.2021 IEEE MIT Undergraduate Research Technology Conference URTC20211510.1109/URTC54388.2021.9701619
     [Google Scholar]
  37. FekiI. AmmarS. KessentiniY. MuhammadK. Federated learning for COVID-19 screening from chest x-ray images.Appl. Soft Comput.202110610733010.1016/j.asoc.2021.10733033776607
     [Google Scholar]
  38. DouQ. SoT.Y. JiangM. LiuQ. VardhanabhutiV. KaissisG. LiZ. SiW. LeeH.H.C. YuK. FengZ. DongL. BurianE. JungmannF. BrarenR. MakowskiM. KainzB. RueckertD. GlockerB. YuS.C.H. HengP.A. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.NPJ Digit. Med.2021416010.1038/s41746‑021‑00431‑633782526
     [Google Scholar]
  39. FlorescuL.M. StrebaC.T. ŞerbănescuM.S. MămuleanuM. FlorescuD.N. TeicăR.V. NicaR.E. GheoneaI.A. Federated learning approach with pre-trained deep learning models for COVID-19 detection from unsegmented CT images.Life (Basel)202212795810.3390/life1207095835888048
     [Google Scholar]
  40. ElshabrawyK.M. AlfaresM.M. SalemM.A.M. Ensemble Federated Learning for Non-II D COVID-19 Detection.2022 5th International Conference on Computing and Informatics (ICCI)202210.1109/ICCI54321.2022.9756090
     [Google Scholar]
  41. KandatiD.R. GadekalluT.R. Federated learning approach for early detection of chest lesion caused by COVID-19 infection using particle swarm optimization.Electronics202312371010.3390/electronics12030710
     [Google Scholar]
  42. MalikH. NaeemA. NaqviR.A. LohW.K. DMFL_Net: A federated learning-based framework for the classification of COVID-19 from multiple chest diseases using x-rays.Sensors202323274310.3390/s2302074336679541
     [Google Scholar]
  43. MooneyP. Chest X-ray images (pneumonia).2018Available from: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
  44. Pneumonia binary classification, Kaggle.2022Available from: https://www.kaggle.com/code/pushkalpandey3/pneumonia-binary-classification/data
  45. ChakrabortyP. Pneumonia chest X ray.2018Available from: https://www.kaggle.com/datasets/parthachakraborty/pneumonia-chest-x-ray
/content/journals/cmir/10.2174/0115734056333970241212132150
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FedPneu: Federated Learning for Pneumonia Detection across Multiclient Cross-Silo Healthcare Datasets
Curr. Med. Imaging 21, E15734056333970 (2025); https://doi.org/10.2174/0115734056333970241212132150
/content/journals/cmir/10.2174/0115734056333970241212132150
/content/journals/cmir/10.2174/0115734056333970241212132150
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  • Article Type:     Research Article
Keyword(s): Computer-aided diagnosis; Federated learning; FedPneu model; Machine vision in medicine; Medical image visualization; Pneumonia detection

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