脑肿瘤是致命的，并且随着进展严重扰乱大脑功能。及时检测和精确监测对于改善患者治疗效果和生存至关重要。利用医疗物联网 (IoMT) 的智能医疗保健系统通过提供简化的远程医疗保健彻底改变了患者护理，特别是对于患有脑肿瘤等急性疾病的个人。然而，此类系统面临重大挑战，例如（1）在不断扩大的数字医疗保健领域网络攻击日益普遍，以及（2）现有肿瘤检测方法缺乏可靠性和准确性。为了解决这些问题，我们提出了安全脑肿瘤检测（SBTD），这是第一个将 IoMT 与安全肿瘤检测相结合的统一系统。 SBTD 的特点：（1）基于混沌理论的强大安全框架，以保护医疗数据； (2) 可靠的基于机器学习的肿瘤检测框架，可利用肿瘤的解剖结构准确定位肿瘤。对不同多模态 MRI 数据集的综合实验评估证明了该系统的适用性、临床适用性以及优于最先进算法的性能。

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.