Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application

Roman Rodriguez-Aguilar (), Jose-Antonio Marmolejo-Saucedo and Utku Köse
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Roman Rodriguez-Aguilar: Facultad de Ciencias Economicas y Empresariales, Universidad Panamericana, Ciudad de Mexico 03920, Mexico
Jose-Antonio Marmolejo-Saucedo: Romway Machinery Manufacturing Co., Ltd., No. 16 Julong Road, Huangze Industrial Park, Shengzhou 312400, China
Utku Köse: Faculty of Engineering and Natural Sciences, Suleyman Demirel University, Isparta 32260, Turkey

Mathematics, 2025, vol. 13, issue 15, 1-25

Abstract: Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process.

Keywords: federated learning; Internet of Medical Things; diagnosis; brain tumor; CapsNet; digital health (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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