A Collaborative Federated Learning Framework for Lung and Colon Cancer Classifications
Open accessAbstract
Lung and colon cancers are common types of cancer with significant fatality rates. Early identification considerably improves the odds of survival for those suffering from these diseases. Histopathological image analysis is crucial for detecting cancer by identifying morphological anomalies in tissue samples. Regulations such as the HIPAA and GDPR impose considerable restrictions on the sharing of sensitive patient data, mostly because of privacy concerns. Federated learning (FL) is a promising technique that allows the training of strong models while maintaining data privacy. The use of a federated learning strategy has been suggested in this study to address privacy concerns in cancer categorization. To classify histopathological images of lung and colon cancers, this methodology uses local models with an Inception-V3 backbone. The global model is then updated on the basis of the local weights. The images were obtained from the LC25000 dataset, which consists of five separate classes. Separate analyses were performed for lung cancer, colon cancer, and their combined classification. The implemented model successfully classified lung cancer images into three separate classes with a classification accuracy of 99.867%. The classification of colon cancer images was achieved with 100% accuracy. More significantly, for the lung and colon cancers combined, the accuracy reached an impressive 99.720%. Compared with other current approaches, the proposed framework showed an improved performance. A heatmap, visual saliency map, and GradCAM were generated to pinpoint the crucial areas in the histopathology pictures of the test set where the models focused in particular during cancer class predictions. This approach demonstrates the potential of federated learning to enhance collaborative efforts in automated disease diagnosis through medical image analysis while ensuring patient data privacy.
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A federated learning framework combining decentralized local model weights from several medical facilities classified lung and colon cancer with as much as 99.7% accuracy while adhering to data privacy laws.
“Similarly, by combining decentralized local model weights from several medical facilities, an FL framework was able to classify lung and colon cancer with as much as 99.7% accuracy, demonstrating how it can improve the performance while adhering to data privacy laws [5].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [5]
The Abstract reports 99.720% accuracy for combined lung and colon cancer classification, and Data and Methodology describes aggregating locally trained models without sharing patient data. Comparative Analysis says the framework used five clients to demonstrate federated learning; the copy does not establish that several actual medical facilities participated. Limitations and Future Work also notes unresolved risks to patient-data privacy, so adherence to data privacy laws is not demonstrated. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEarly cancer diagnosis accuracy has been shown to significantly improve with federated learning.
“Early cancer diagnosis accuracy has been shown to significantly improve with FL.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [5]
The Abstract reports high accuracy for federated classification of lung and colon cancer images and says the framework performed better than other approaches. It does not show that federated learning significantly improved the accuracy of early-stage cancer diagnosis; early diagnosis is discussed as a potential benefit in the Introduction, not evaluated as an outcome. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFederated learning has enabled multi-institutional partnerships in disease prediction that have improved model generalizability by 20% while adhering to stringent data-sharing laws such as GDPR.
“FL has enabled multi-institutional partnerships in disease prediction that have improved model generalizability by 20% while adhering to stringent data-sharing laws such as GDPR [5].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [5]
The Data and Methodology section describes federated training across healthcare institutions without sharing patient data. The Limitations and Future Work section treats improved generalizability as a possibility for future work, not a measured 20% gain; the Challenges section identifies GDPR compliance as an ongoing concern rather than demonstrating compliance. No page numbers are available in our copy. (LLM, conf=0.89)
Experimental — accuracy not yet measured
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Nelson, A. H., Suh, E. J., Nobuhara, C. K., & Grantcharov, T. (2026) · Thoracic surgery clinics/Thorac. surg. clin. · doi:10.1016/j.thorsurg.2026.09.011
- Authors
- Hossain, M. M., Islam, M. R., Ahamed, M. F., Ahsan, M., & Haider, J.
- Affiliations
- Rajshahi University of Engineering and Technology (BD)
- University of York (GB)
- Manchester Metropolitan University (GB)
- Corresponding author
- Hossain, Md. Munawar
- Haider, Julfikar
- Venue
- Technologies
- Publisher
- MDPI AG
- Published
- 2024-09-04
- Volume
- 12
- Issue
- 9
- Pages
- 151
- ISSN
- 2227-7080 (ISSN-L)
- OpenAlex
- W4402224479
- Cited by
- 44
- References
- 43
- Citations by year
- 2026: 24 · 2025: 19 · 2024: 1
- FWCI
- 6.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Technologies (publishedVersion, cc-by)
- e-space (Manchester Metropolitan University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- federated learning, lung cancer classification, colon cancer classification, histopathology image analysis, InceptionV3, data privacy, Grad-CAM, visual saliency map, automated cancer diagnosis, LC25000 dataset
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