Development and Performance Evaluation of Intelligent Model for Enhanced Detection of IoMT Enabled Brain Tumor

Abstract

The accurate diagnosis of brain tumors remains a major challenge in medical imaging because tumor structures vary in size, shape, and appearance. Traditional methods such as manual segmentation and classification are time-consuming and may produce inconsistent results due to observer variation. This paper presents an approach that combines the Internet of Medical Things (IoMT) with deep learning models to improve the accuracy and efficiency of brain tumor diagnosis.
IoMT enables the collection and transfer of Magnetic Resonance Imaging (MRI) data to cloud platforms for real-time analysis and automated processing. The proposed framework uses Swin UNet for segmentation of tumor regions and EfficientNet-V2 for classification into tumor subtypes. Swin UNet uses transformer-based attention mechanisms to capture multi-scale spatial features, while EfficientNet-V2 supports efficient feature learning for classification. Experiments performed on the BRATS 2020 dataset demonstrate the effectiveness of the framework, achieving a segmentation Intersection over Union (IoU) of 78% and a classification accuracy of 97%. The integration of IoMT supports remote accessibility, faster clinical decision-making, reduced manual effort, and automated workflows. The proposed framework improves reliability and performance compared to conventional CNN-based systems. This study highlights the potential of AI-driven medical diagnosis and provides a scalable solution for practical healthcare applications. Future work will focus on real-time deployment and extension to other medical imaging tasks. The framework also reduces computational complexity and supports patient data, making it suitable for clinical environments where quick and accurate diagnosis is important for effective treatment planning, better patient management, and improved healthcare services today globally.