SBTD: Secured Brain Tumor Detection in IoMT Enabled Smart Healthcare

Area of Science:

• Medical Imaging and Diagnostics
• Cybersecurity in Healthcare
• Artificial Intelligence in Medicine

Background:

• Brain tumors significantly impair neurological function and survival rates.
• Remote healthcare systems, like those using the Internet of Medical Things (IoMT), offer improved patient monitoring.
• Existing IoMT healthcare systems face challenges including cybersecurity threats and inaccurate tumor detection.

Purpose of the Study:

• To introduce the Secured Brain Tumor Detection (SBTD) system, a novel integration of IoMT and secure tumor detection.
• To address the critical needs for enhanced data security and diagnostic accuracy in brain tumor management.
• To provide a reliable and clinically applicable solution for remote brain tumor monitoring and detection.

Main Methods:

• Developed a robust security framework utilizing chaos theory to protect sensitive medical data.
• Implemented a machine learning-based tumor detection framework for accurate anatomical localization of brain tumors.
• Conducted comprehensive experimental evaluations using multimodal MRI datasets.

Main Results:

• The SBTD system demonstrated superior performance compared to existing state-of-the-art algorithms.
• Experimental results validated the system's accuracy in localizing brain tumors.
• The proposed security framework effectively safeguarded medical data against cyber threats.

Conclusions:

• SBTD offers a unified, secure, and reliable solution for brain tumor detection and monitoring.
• The system shows significant clinical applicability and potential to improve patient outcomes.
• Integrating IoMT with advanced security and AI enhances the future of smart healthcare for neurological conditions.