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FedFusionNet: Advancing oral cancer recurrence prediction through federated fusion modeling.

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Title: FedFusionNet: Advancing oral cancer recurrence prediction through federated fusion modeling.
Authors: Aurnob, Al Rafi1 (AUTHOR), Tanim, Sharia Arfin1 (AUTHOR) 20-42096-1@student.aiub.edu, Shrestha, Tahmid Enam1 (AUTHOR), Mridha, M.F.1 (AUTHOR) firoz.mridha@aiub.edu, Mistry, Durjoy2 (AUTHOR)
Source: Information Fusion. Aug2026, Vol. 132, pN.PAG-N.PAG. 1p.
Subjects: Oral cancer, Federated learning, Medical informatics, Data protection, Artificial intelligence, Machine learning, Diagnostic imaging
Abstract: • FedFusionNet: a custom single-level fusion model for accurate oral cancer diagnosis. • Proof-of-concept federated learning framework designed for privacy-preserving decentralized training. • Designed to handle both IID and non-IID data distributions. • Outperforms baseline federated models with statistically significant improvements. • XAI techniques provide clinical interpretability. Oral cancer represents a considerable global medical problem that requires the development of new technologies that offer reliable advanced therapies. This study introduced FedFusionNet, a fusion-centric model that was meticulously developed to advance early oral cancer diagnosis while preserving data privacy. The primary objective was to develop a model using federated learning (FL) to train across diverse healthcare facilities globally without compromising patient data confidentiality. This model uses features from the ResNeXt101 32X8D and InceptionV3 models to implement a single-level fusion via feature concatenation. This helps to enhance the effectiveness and stability of the model. Specifically, the federated averaging (FedAvg) technique fosters collaborative model training across multiple hospitals while safeguarding sensitive patient information. This ensured that each participating hospital could contribute to the development of the model without sharing the raw data. The proposed model was trained on a dataset of 10,002 images that included both healthy and cancerous oral tissues. Rigorous training and evaluation were conducted for both Independent and Identically Distributed (IID) and Independent and Non-Identically Distributed (Non-IID) settings. FedFusionNet demonstrated superior performance compared with pre-trained and some custom models for oral cancer diagnosis. This scalable and secure framework has profound implications for healthcare analytics. It is a proof-of-concept demonstration that utilizes publicly available data to establish the technical feasibility of the FedFusionNet framework. Future deployment in actual collaborative environments would demonstrate its security-by-design capabilities across hospitals, where patient data confidentiality is a priority. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• FedFusionNet: a custom single-level fusion model for accurate oral cancer diagnosis. • Proof-of-concept federated learning framework designed for privacy-preserving decentralized training. • Designed to handle both IID and non-IID data distributions. • Outperforms baseline federated models with statistically significant improvements. • XAI techniques provide clinical interpretability. Oral cancer represents a considerable global medical problem that requires the development of new technologies that offer reliable advanced therapies. This study introduced FedFusionNet, a fusion-centric model that was meticulously developed to advance early oral cancer diagnosis while preserving data privacy. The primary objective was to develop a model using federated learning (FL) to train across diverse healthcare facilities globally without compromising patient data confidentiality. This model uses features from the ResNeXt101 32X8D and InceptionV3 models to implement a single-level fusion via feature concatenation. This helps to enhance the effectiveness and stability of the model. Specifically, the federated averaging (FedAvg) technique fosters collaborative model training across multiple hospitals while safeguarding sensitive patient information. This ensured that each participating hospital could contribute to the development of the model without sharing the raw data. The proposed model was trained on a dataset of 10,002 images that included both healthy and cancerous oral tissues. Rigorous training and evaluation were conducted for both Independent and Identically Distributed (IID) and Independent and Non-Identically Distributed (Non-IID) settings. FedFusionNet demonstrated superior performance compared with pre-trained and some custom models for oral cancer diagnosis. This scalable and secure framework has profound implications for healthcare analytics. It is a proof-of-concept demonstration that utilizes publicly available data to establish the technical feasibility of the FedFusionNet framework. Future deployment in actual collaborative environments would demonstrate its security-by-design capabilities across hospitals, where patient data confidentiality is a priority. [ABSTRACT FROM AUTHOR]
ISSN:15662535
DOI:10.1016/j.inffus.2026.104205