ArticleDiagnostics (Basel, Switzerland)2023
Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions.Journal of imaging · 2026Review
- Federated learning with integrated attention multiscale model for brain tumor segmentation.Scientific reports · 2025Article
- The application of artificial intelligence in upper gastrointestinal cancers.Journal of the National Cancer Center · 2025Review
- GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in Healthcare.F1000Research · 2025Article
- An optimized transfer learning approach integrating deep convolutional feature extractors for malaria parasite classification in erythrocyte microscopy.Frontiers in medicine · 2025Article
- Common laboratory parameters as predictors of prognosis in primary lung cancer.Frontiers in oncology · 2025Article
- Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning.BMC medical informatics and decision making · 2024Article
- Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram.Diagnostics (Basel, Switzerland) · 2023Article
- A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma.Diagnostics (Basel, Switzerland) · 2023Article
- Impact of Aggregation Functions and Learning Settings in Privacy-Preserving Federated Learning for Skin Cancer Detection.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
The early detection and classification of lung cancer is crucial for improving a patient's outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.
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