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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
405

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Brain Image Segmentation in Recent Years: A Narrative Review.

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Automated brain segmentation using deep learning and hybrid metaheuristic methods offers efficient tumor detection. However, these advanced techniques require significant computational resources and memory.

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Brain image segmentation is critical for diagnosing neurological disorders.
  • Increased prevalence of brain disorders necessitates automated solutions.
  • Current manual segmentation is time-consuming and labor-intensive.

Purpose of the Study:

  • To critically review recent segmentation and classification methods for brain MRI.
  • To discuss common issues, advantages, and disadvantages of existing techniques.
  • To identify efficient approaches for brain tumor segmentation.

Main Methods:

  • Review of intensity-based, machine learning, metaheuristic, deep learning, and hybrid segmentation approaches.
  • Analysis of computational complexity and memory requirements.
  • Comparative assessment of various brain image segmentation techniques.

Main Results:

  • Deep learning and hybrid metaheuristic methods show high efficiency in brain tumor segmentation.
  • These advanced methods present challenges in computation and memory complexity.
  • A comprehensive understanding of method strengths and limitations is provided.

Conclusions:

  • Deep learning and hybrid metaheuristic approaches are promising for reliable brain tumor segmentation.
  • Addressing computational and memory demands is crucial for clinical implementation.
  • Further research is needed to optimize these methods for practical use.