Treffer: SBTD: Secured Brain Tumor Detection in IoMT Enabled Smart Healthcare.

Title:
SBTD: Secured Brain Tumor Detection in IoMT Enabled Smart Healthcare.
Source:
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2026 Jan; Vol. 30 (1), pp. 39-50.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101604520 Publication Model: Print Cited Medium: Internet ISSN: 2168-2208 (Electronic) Linking ISSN: 21682194 NLM ISO Abbreviation: IEEE J Biomed Health Inform Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, 2013-
Entry Date(s):
Date Created: 20241016 Date Completed: 20260108 Latest Revision: 20260112
Update Code:
20260113
DOI:
10.1109/JBHI.2024.3482465
PMID:
39412974
Database:
MEDLINE

Weitere Informationen

Brain tumors are fatal and severely disrupt brain function as they advance. Timely detection and precise monitoring are crucial for improving patient outcomes and survival. A smart healthcare system leveraging the Internet of Medical Things (IoMT) revolutionizes patient care by offering streamlined remote healthcare, especially for individuals with acute medical conditions like brain tumors. However, such systems face significant challenges, such as 1) the increasing prevalence of cyber attacks in the expanding digital healthcare landscape, and 2) the lack of reliability and accuracy in existing tumor detection methods. To address these issues, we propose Secured Brain Tumor Detection (SBTD), the first unified system integrating IoMT with secure tumor detection. SBTD features: 1) a robust security framework grounded in chaos theory, to safeguard medical data; and 2) a reliable machine learning-based tumor detection framework that accurately localizes tumors using their anatomy. Comprehensive experimental evaluations on different multimodal MRI datasets demonstrate the system's suitability, clinical applicability and superior performance over state-of-the-art algorithms.