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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning-assisted IoMT framework for cerebral microbleed detection.

Zeeshan Ali1, Sheneela Naz2, Sadaf Yasmin3

  • 1Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan.

Heliyon
|December 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-driven Internet of Medical Things (IoMT) framework for accurate brain MRI analysis. The enhanced UNet model effectively detects cerebral microbleeds (CMBs) without pre-processing, improving diagnostic accuracy.

Keywords:
Cerebral microbleed (CMB) segmentationComputer-aided diagnostic (CAD) systemsDeep learningInternet of medical thingsUNet

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Internet of Medical Things

Background:

  • Cerebral microbleeds (CMBs) are difficult to detect in brain MRIs due to their small size and similarity to healthy tissue.
  • Accurate CMB detection is crucial for diagnosis and therapy, especially in underserved areas.
  • Existing computer-aided diagnostic (CAD) systems for CMBs involve multi-stage processes, hindering full automation.

Purpose of the Study:

  • To develop an end-to-end Internet of Medical Things (IoMT) framework for automated cerebral microbleed (CMB) detection and segmentation using brain MRI.
  • To address the limitations of existing CAD systems by proposing a model that requires no pre- or post-processing steps.
  • To enhance the accuracy and efficiency of CMB detection in intelligent medical systems.

Main Methods:

  • An enhanced UNet-based deep learning model was developed for direct CMB detection and segmentation from brain MRI scans.
  • The proposed IoMT framework integrates advanced sensors with AI-powered insights for real-time medical analysis.
  • The model was designed to process complete MRI images without manual intervention or complex pre-processing.

Main Results:

  • The proposed model achieved a Dice score of 0.70, indicating effective segmentation of CMBs.
  • The system demonstrated high accuracy (99%) and a very low false-positive rate (0.002%) in CMB detection.
  • The method successfully detected CMBs despite contrast variations and similarity to normal tissues, outperforming existing approaches.

Conclusions:

  • The developed end-to-end UNet-based IoMT framework offers a robust solution for accurate and automated CMB detection in brain MRIs.
  • This approach significantly improves diagnostic capabilities, particularly in settings lacking specialist expertise.
  • The findings pave the way for more intelligent and reliable digital medical services in neuroimaging.