Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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Advancements in Liver Tumor Diagnosis through Deep Federated Learning and Optimization Methods

Authors
Suvarna Jagtap1, *, Aquila Shaikh2, Madhuri Pant3
1Yashwantrao Mohite College of Arts, Science and Commerce, Bharati Vidyapeeth (Deemed University), Pune, 411038, India
2Late Bhausaheb Hiray S.S. Trust‘s Institute of Computer Application, Bandra East, Mumbai, 400051, India
3Vishwakarma University, Pune, India
*Corresponding author.
Corresponding Author
Suvarna Jagtap
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_53How to use a DOI?
Keywords
Federated Learning; Spiking Neural Networks; Medical Image Segmentation; Neutrosophic Logic
Abstract

Liver cancer ranks among the most fatal tumors globally, necessitating precise and prompt detection systems. . Federated learning (FL) provides an effective approach by facilitating collaborative model training among decentralized institutions while preserving sensitive patient data confidentiality. This study reviews Deep Federated Machine Learning (DFML) strategies along with optimization methods to address such challenges. A Hybrid Diagnostic pipeline is proposed combining Spiking Neural Networks (SNN), Federated Learning (FL), and Neutrosophic Logic (NL). Tests performed on a Kaggle liver tumor dataset indicate that the approach achieved a Dice Coefficient of 95.8% and stronger classification performance compared with conventional Fuzzy c-means (FCM) and CNN models, while maintaining patient confidential data.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_53How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Suvarna Jagtap
AU  - Aquila Shaikh
AU  - Madhuri Pant
PY  - 2026
DA  - 2026/01/06
TI  - Advancements in Liver Tumor Diagnosis through Deep Federated Learning and Optimization Methods
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 765
EP  - 772
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_53
DO  - 10.2991/978-94-6463-948-3_53
ID  - Jagtap2026
ER  -