{"title":"SBTD:IoMT 智能医疗中的安全脑肿瘤检测。","authors":"Nishtha Tomar, Parkala Vishnu Bharadwaj Bayari, Gaurav Bhatnagar","doi":"10.1109/JBHI.2024.3482465","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SBTD: Secured Brain Tumor Detection in IoMT Enabled Smart Healthcare.\",\"authors\":\"Nishtha Tomar, Parkala Vishnu Bharadwaj Bayari, Gaurav Bhatnagar\",\"doi\":\"10.1109/JBHI.2024.3482465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":13073,\"journal\":{\"name\":\"IEEE Journal of Biomedical and Health Informatics\",\"volume\":\"PP \",\"pages\":\"\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Biomedical and Health Informatics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/JBHI.2024.3482465\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Biomedical and Health Informatics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/JBHI.2024.3482465","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
SBTD: Secured Brain Tumor Detection in IoMT Enabled Smart Healthcare.
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.
期刊介绍:
IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.