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Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS.

Parvathaneni Naga Srinivasu1, Valentina Emilia Balas2

  • 1Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India.

Peerj. Computer Science
|August 26, 2021
PubMed
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This study introduces an automated brain MRI segmentation method for improved medical diagnosis. The novel approach achieves 77% accuracy with minimal training, outperforming traditional methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Advancements in medical imaging, particularly Magnetic Resonance Imaging (MRI), present challenges for radiologists in accurate ailment assessment.
  • Automated segmentation of brain abnormalities is crucial for efficient diagnosis and analysis.
  • Current supervised learning models often require extensive training data and computational resources.

Purpose of the Study:

  • To develop a computationally efficient, automated segmentation approach for assessing damaged brain regions using MRI.
  • To create a self-learning model requiring minimal training for improved diagnostic workflows.
  • To enhance the accuracy and convenience of abnormality investigation and statistical analysis.

Main Methods:

Keywords:
CNNHARISMRI imagesMachine learningSelf-Learning Network

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  • Development of a novel automated segmentation approach for brain MRI.
  • Implementation of a self-learning mechanism that utilizes previous experimental outcomes for model improvement.
  • Comparison with conventional supervised learning strategies, including Convolutional Neural Network (CNN) deep learning models.

    • Main Results:

    • The proposed automated segmentation approach achieved an accuracy of 77% with minimal model training.
    • The method demonstrated computational efficiency compared to other supervised learning strategies.
    • Performance was validated using metrics such as sensitivity, specificity, Jaccard Similarity Index, and Matthews correlation coefficient.

    Conclusions:

    • The developed automated segmentation model offers a promising, efficient, and accurate solution for brain MRI analysis.
    • Minimal training requirements make the model highly adaptable and practical for clinical settings.
    • The approach facilitates more comfortable and convenient investigation and statistical analysis of brain abnormalities.