FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases
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 a...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Computer Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1633916/full |
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| author | Sivan Durga Esther Daniel Surleese Seetha Vijaya Kumar Reshma Vasily Sachnev |
| author_facet | Sivan Durga Esther Daniel Surleese Seetha Vijaya Kumar Reshma Vasily Sachnev |
| author_sort | Sivan Durga |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-630dc78bfffa49acbd2da8c3aa6eeb43 |
| institution | Kabale University |
| issn | 2624-9898 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj-art-630dc78bfffa49acbd2da8c3aa6eeb432025-08-20T03:39:26ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982025-08-01710.3389/fcomp.2025.16339161633916FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseasesSivan Durga0Esther Daniel1Surleese Seetha2Vijaya Kumar Reshma3Vasily Sachnev4TIFAC CORE in Cyber Security, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaDivision of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, IndiaDepartment of ISE, CMR Institute of Technology, Bengaluru, IndiaComputer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, IndiaDepartment of Information, Communication and Electronics Engineering, Catholic University of Korea, Korea, Republic of KoreaThe 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.https://www.frontiersin.org/articles/10.3389/fcomp.2025.1633916/fullcentral server based learning modelfederated learningensemble modelexplainable AISHapley Additive exPlanationsgrad-CAM |
| spellingShingle | Sivan Durga Esther Daniel Surleese Seetha Vijaya Kumar Reshma Vasily Sachnev FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases Frontiers in Computer Science central server based learning model federated learning ensemble model explainable AI SHapley Additive exPlanations grad-CAM |
| title | FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases |
| title_full | FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases |
| title_fullStr | FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases |
| title_full_unstemmed | FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases |
| title_short | FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases |
| title_sort | flem xai federated learning based real time ensemble model with explainable ai framework for an efficient diagnosis of lung diseases |
| topic | central server based learning model federated learning ensemble model explainable AI SHapley Additive exPlanations grad-CAM |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1633916/full |
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