Medical Image Classification with Privacy: Centralized and Federated Learning Comparison

Leonardo Gabriel Ferreira Rodrigues; Gleidson V. Gomes Barbosa; Rodrigo Moreira; Larissa Ferreira Rodrigues Moreira; André Ricardo Backes

doi:10.22456/2175-2745.143478

Authors

Leonardo Gabriel Ferreira Rodrigues Federal University of Uberlândia (UFU) https://orcid.org/0009-0003-7246-2175
Gleidson V. Gomes Barbosa Federal University of Uberlândia (UFU) https://orcid.org/0009-0000-2159-335X
Rodrigo Moreira Federal University of Viçosa (UFV)
Larissa Ferreira Rodrigues Moreira Federal University of Viçosa (UFV) https://orcid.org/0000-0001-8947-9182
André Ricardo Backes Federal University of São Carlos (UFSCar) https://orcid.org/0000-0002-7486-4253

DOI:

https://doi.org/10.22456/2175-2745.143478

Keywords:

Federated Learning, Centralized Learning, Image Classification, Cancer, Deep Learning

Abstract

The use of different techniques in image-based medical diagnostic computing has positively impacted the healthcare industry. Diseases such as colon and lung cancer can be detected using image classification strategies. In this context, Federated Learning (FL) has emerged as an enabling approach to disease detection with security and privacy when handling sensitive patient data. This study aimed to compare the centralized and FL classifications of histopathological images of lung and colon cancer. We compared the performance of FL with that of centralized learning, and the results indicated that FL outperformed centralized approaches in scenarios with heterogeneous datasets. These findings highlight the potential of FL as an alternative to medical image classification, particularly in privacy-sensitive applications.

Downloads

Download data is not yet available.

References

SIEGEL, R. L. et al. Cancer statistics, 2023. CA: A Cancer Journal for Clinicians, v. 73, n. 1, p. 17–48, 2023. Disponível em: ⟨https://acsjournals.onlinelibrary.wiley.com/doi/abs/10.3322/caac.21763⟩.

(WHO), W. H. O. Global Cancer Observatory. Acesso em agosto/2024. Disponível em: ⟨https://gco.iarc.fr/en⟩.

SCHWALBE, N.; WAHL, B. Artificial intelligence and the future of global health. The Lancet, v. 395, n. 10236, p. 1579–1586, 2020. ISSN 0140-6736. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0140673620302269⟩.

SOHAN, M. F.; BASALAMAH, A. A Systematic Review on Federated Learning in Medical Image Analysis. IEEE Access, v. 11, p. 28628–28644, 2023.

RODRIGUES MOREIRA, L. F. et al. An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis. Applied Soft Computing, v. 134, p. 110014, 2023. ISSN 1568-4946. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S1568494623000327⟩.

COELHO, K. K. et al. A survey on federated learning for security and privacy in healthcare applications. Computer Communications, v. 207, p. 113–127, 2023. ISSN 0140-3664. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S014036642300172X⟩.

GUAN, H. et al. Federated learning for medical image analysis: A survey. Pattern Recognition, v. 151, p. 110424, 2024. ISSN 0031-3203. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0031320324001754⟩.

KONEČNÝ, J. et al. Federated Optimization: Distributed Machine Learning for On-Device Intelligence. CoRR, abs/1610.02527, 2016. Disponível em: ⟨http://arxiv.org/abs/1610.02527⟩.

MCMAHAN, B. et al. Communication-Efficient Learning of Deep Networks from Decentralized Data. In: SINGH, A.; ZHU, J. (Ed.). Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. PMLR, 2017. (Proceedings of Machine Learning Research, v. 54), p. 1273–1282. Disponível em: ⟨https://proceedings.mlr.press/v54/mcmahan17a.html⟩.

ANTICO, T.; MOREIRA, L.; MOREIRA, R. Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction. In: Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional. Porto Alegre, RS, Brasil: SBC, 2022. p. 282–293. ISSN 2763-9061. Disponível em: ⟨https://sol.sbc.org.br/index.php/eniac/article/view/22789⟩.

BLIKA, A. et al. Federated Learning For Enhanced Cybersecurity And Trustworthiness In 5G and 6G Networks: A Comprehensive Survey. IEEE Open Journal of the Communications Society, p. 1–1, 2024.

REHMAN, A. et al. Fedcscd-gan: A secure and collaborative framework for clinical cancer diagnosis via optimized federated learning and gan. Biomedical Signal Processing and Control, Elsevier, v. 89, p. 105893, 2024.

ADNAN, M. et al. Federated learning and differential privacy for medical image analysis. Scientific Reports, Nature Publishing Group UK London, v. 12, n. 1, p. 1953, 2022.

NERGIZ, M. Federated learning-based colorectal cancer classification by convolutional neural networks and general visual representation learning. International Journal of Imaging Systems and Technology, Wiley Online Library, v. 33, n. 3, p. 951–964, 2023.

TAN, Y. N. et al. A transfer learning approach to breast cancer classification in a federated learning framework. IEEE Access, IEEE, v. 11, p. 27462–27476, 2023.

SUBASHCHANDRABOSE, U. et al. Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer. Diagnostics, v. 13, n. 19, 2023. ISSN 2075-4418. Disponível em: ⟨https://www.mdpi.com/2075-4418/13/19/3053⟩.

AGBLEY, B. L. Y. et al. Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things. IEEE Journal of Biomedical and Health Informatics, v. 28, n. 6, p. 3389–3400, 2024.

HOSSAIN, M. M. et al. A Collaborative Federated Learning Framework for Lung and Colon Cancer Classifications. Technologies, v. 12, n. 9, 2024. ISSN 2227-7080. Disponível em: ⟨https://www.mdpi.com/2227-7080/12/9/151⟩.

BORKOWSKI, A. A. et al. Lung and Colon Cancer Histopathological Image Dataset (LC25000). 2019. Disponível em: ⟨https://arxiv.org/abs/1912.12142⟩.

DAZA ORTIZ, Z. Lung and Colon Cancer Histopathological Images (LC25000). Acesso em agosto/2024. Disponível em: ⟨https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images⟩.

GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. [S.l.]: MIT Press, 2016. ⟨http://www.deeplearningbook.org⟩.

PONTI, M. A. et al. Everything you wanted to know about deep learning for computer vision but were afraid to ask. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T). [S.l.: s.n.], 2017. p. 17–41. ISSN 2474-0705.

IANDOLA, F. N. et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. 2016.

MOREIRA, R. et al. An intelligent native network slicing security architecture empowered by federated learning. Future Generation Computer Systems, v. 163, p. 107537, 2025. ISSN 0167-739X. Disponível em: ⟨https://www.sciencedirect.com/science/article/pii/S0167739X24005016⟩.

TAN, P.-N. et al. Introduction to Data Mining (2nd Edition). 2nd. ed. [S.l.]: Pearson, 2018. ISBN 0133128903.

BALDIN, I. et al. FABRIC: A National-Scale Programmable Experimental Network Infrastructure. IEEE Internet Computing, v. 23, n. 6, p. 38–47, 2019.

PASZKE, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In: WALLACH, H. et al. (Ed.). Advances in Neural Information Processing Systems 32. [S.l.]: Curran Associates, Inc., 2019. p. 8024–8035.

BEUTEL, D. J. et al. Flower: A Friendly Federated Learning Research Framework. CoRR, abs/2007.14390, 2020. Disponível em: ⟨https://arxiv.org/abs/2007.14390⟩.