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Related Experiment Video

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Brain pathology identification using computer aided diagnostic tool: A systematic review.

Anjan Gudigar1, U Raghavendra1, Ajay Hegde2

  • 1Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.

Computer Methods and Programs in Biomedicine
|December 2, 2019
PubMed
Summary
This summary is machine-generated.

Computer-aided diagnosis (CAD) improves patient care by reducing errors. This survey reviews brain pathology identification (BPI) algorithms using MRI, offering insights for future research to enhance diagnostic accuracy.

Keywords:
Brain pathologyClassificationComputer aided diagnosticDeep learningFeature extractionMagnetic resonance imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Computer-aided diagnosis (CAD) systems enhance patient quality of life by minimizing diagnostic errors.
  • Rapid identification of brain pathology is crucial for managing life-threatening neurological conditions.
  • Brain pathology identification (BPI) using medical imaging is an active area of research.

Purpose of the Study:

  • To systematically survey contemporary brain pathology identification (BPI) algorithms.
  • To provide a synopsis of recent literature on BPI using brain magnetic resonance imaging (MRI).
  • To indicate future research directions for improving automatic BPI performance.

Main Methods:

  • Systematic literature review of BPI algorithms.
  • Analysis of image processing techniques applied to brain MRI.
  • Categorization and summarization of contemporary BPI methods.

Main Results:

  • An overview of various BPI algorithms is presented.
  • Recent advancements in automatic BPI using MRI are summarized.
  • The survey highlights the importance of refining image processing algorithms.

Conclusions:

  • Continuous refinement of image processing algorithms is essential for advancing automatic BPI.
  • The surveyed literature provides a foundation for future research in neurological disease diagnosis.
  • This work aims to guide investigators in the field of brain pathology identification.