{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:25:26Z","timestamp":1771547126220,"version":"3.50.1"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:p>The computer-aided diagnosis helps medical professionals detect and classify lung diseases from chest X-rays by leveraging medical image processing and central server-based machine learning models. These technologies provide real-time assistance to analyze the input and help efficiently detect the abnormalities at the earliest. However, traditional learning models are not suitable for live scenarios that require privacy, data diversity, and decentralized processing. The Federated learning-based model facilitates the protection of medical data privacy while processing a large volume of medical images, aiming to improve the overall efficiency of the model. This paper proposes a Federated Learning based Ensemble Model (FLEM) framework for an efficient diagnosis of lung diseases. The FLEM utilizes explainable AI techniques, including SHAP, Grad-CAM, and Differential Privacy, to provide transparency and interpretability of predictions while maintaining the privacy and security of medical data. We applied InceptionV3, Conv2D, VGG16, and ResNet-50 models on the COVID-19, TB, and pneumonia datasets and analysed the performance of the models in FLEM and Central Server-based Learning Model (CSLM). The performance analysis shows that the FLEM model outperformed the traditional CSLM model in terms of accuracy, training time, and bandwidth consumption. CSLM witnesses a quicker convergence time than FLEM. Although the CSLM model converged after a considerable number of epochs, it resulted in a 5, 8, 9, and 10% accuracy reduction compared to the FLEM-based training of InceptionV3, Conv2D, VGG16, and ResNet50 that achieved accuracies of 91.8, 88, 92.5, and 95.5%, respectively.<\/jats:p>","DOI":"10.3389\/fcomp.2025.1633916","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T05:24:49Z","timestamp":1754889889000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases"],"prefix":"10.3389","volume":"7","author":[{"given":"Sivan","family":"Durga","sequence":"first","affiliation":[]},{"given":"Esther","family":"Daniel","sequence":"additional","affiliation":[]},{"given":"Surleese","family":"Seetha","sequence":"additional","affiliation":[]},{"given":"Vijaya Kumar","family":"Reshma","sequence":"additional","affiliation":[]},{"given":"Vasily","family":"Sachnev","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1007\/s10489-020-01829-7","article-title":"Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network","volume":"51","author":"Abbas","year":"2021","journal-title":"Appl. Intell."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"105350","DOI":"10.1016\/j.compbiomed.2022.105350","article-title":"COVID-19 image classification using deep learning: advances, challenges and opportunities","volume":"144","author":"Aggarwal","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"355","DOI":"10.30574\/wjaets.2024.12.2.0297","article-title":"Prediction of post-covid-19 using supervised machine learning techniques","volume":"12","author":"Akinwamide","year":"2024","journal-title":"World J. Adv. Eng. Technol. Sci."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1186\/s12911-025-02944-6","article-title":"The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions","volume":"25","author":"Alkhanbouli","year":"2025","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1007\/s11831-024-10081-y","article-title":"Chest x-ray images for lung disease detection using deep learning techniques: a comprehensive survey","volume":"31","author":"Al-qaness","year":"2024","journal-title":"Arch. Comp. Methods Eng."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/s40846-020-00529-4","article-title":"Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases","volume":"40","author":"Apostolopoulos","year":"2020","journal-title":"J. Med. Biol. Eng."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"21311","DOI":"10.1007\/s11042-022-13844-6","article-title":"Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures","volume":"82","author":"Arun Prakash","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"6219","DOI":"10.1007\/s00500-023-09480-3","article-title":"Diagnosis and multi-classification of lung diseases in CXR images using optimized deep convolutional neural network","volume":"28","author":"Ashwini","year":"2024","journal-title":"Soft. Comput."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1007\/978-3-031-44084-7_24","article-title":"Weighted average ensemble approach for pediatric pneumonia diagnosis using channel attention deep CNN architectures","volume":"13924","author":"Asswin","year":"2023","journal-title":"Int. Conf. Mining Intellig. Knowledge Explorat."},{"key":"ref10","author":"Bougourzi","year":"2021"},{"key":"ref11","author":"Chaudhary","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","article-title":"Can AI help in screening viral and COVID-19 pneumonia?","volume":"8","author":"Chowdhury","year":"2020","journal-title":"IEEE Access"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat. Med."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"108190","DOI":"10.1016\/j.asoc.2021.108190","article-title":"Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images","volume":"115","author":"de Moura","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref15","author":"Deshmukh","year":"2021"},{"key":"ref16","author":"Durga","year":"2025"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1049\/wss2.12085","article-title":"SmartCardio: advancing cardiac risk prediction through internet of things and edge cloud intelligence","volume":"14","author":"Durga","year":"2024","journal-title":"IET Wireless Sensor Syst."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"107330","DOI":"10.1016\/j.asoc.2021.107330","article-title":"Federated learning for COVID-19 screening from chest X-ray images","volume":"106","author":"Feki","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"15563","DOI":"10.1007\/s00500-023-09167-9","article-title":"Two-stage deep learning model for automate detection and classification of lung diseases","volume":"27","author":"Ganeshkumar","year":"2023","journal-title":"Soft. Comput."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"118869","DOI":"10.1109\/ACCESS.2020.3005510","article-title":"Weakly supervised deep learning for covid-19 infection detection and classification from ct images","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"3563696","DOI":"10.1155\/2023\/3563696","article-title":"Lung diseases detection using various deep learning algorithms","volume":"2023","author":"Jasmine Pemeena Priyadarsini","year":"2023","journal-title":"J. Healthcare Eng."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.compag.2020.105393","article-title":"Deep-learning-based system-scale diagnosis of a nuclear power plant with multiple infrared cameras","volume":"100","author":"Jin","year":"2023","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref23","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"14675","DOI":"10.1109\/ACCESS.2020.2963926","article-title":"Deformation and refined features based lesion detection on chest X-ray","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.ins.2022.01.062","article-title":"Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis","volume":"592","author":"Mahbub","year":"2022","journal-title":"Inf. Sci."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1526221","DOI":"10.3389\/frai.2025.1526221","article-title":"A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity","volume":"8","author":"Mohale","year":"2025","journal-title":"Front. Artif. Intellig."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1016\/j.radi.2022.03.011","article-title":"Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost","volume":"28","author":"Nasiri","year":"2022","journal-title":"Radiography"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/TMI.2020.2995508","article-title":"Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia","volume":"39","author":"Ouyang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref29","author":"Phogat","year":"2023"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"e26218","DOI":"10.1016\/j.heliyon.2024.e26218","article-title":"Lung disease recognition methods using audio-based analysis with machine learning","volume":"10","author":"Sabry","year":"2024","journal-title":"Heliyon"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"104306","DOI":"10.1016\/j.compbiomed.2021.104306","article-title":"Deep learning for diagnosis of COVID-19 using 3D CT scans","volume":"132","author":"Serte","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref32","author":"Sethi","year":"2020"}],"container-title":["Frontiers in Computer Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcomp.2025.1633916\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T05:24:50Z","timestamp":1754889890000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcomp.2025.1633916\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,11]]},"references-count":32,"alternative-id":["10.3389\/fcomp.2025.1633916"],"URL":"https:\/\/doi.org\/10.3389\/fcomp.2025.1633916","relation":{},"ISSN":["2624-9898"],"issn-type":[{"value":"2624-9898","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,11]]},"article-number":"1633916"}}