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Related Concept Videos

Brain Imaging01:14

Brain Imaging

264
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...
264

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Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives.

U Raghavendra1, Anjan Gudigar1, Aritra Paul1

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

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Summary

Early detection of brain tumors is crucial for effective treatment. This review highlights how artificial intelligence (AI) and computer-aided diagnostic (CAD) systems aid in identifying brain tumors, discussing current challenges and future research directions.

Keywords:
Brain tumorCTClassificationDeep learningMRIMachine learningPETSegmentation

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Brain tumors are abnormal growths within the skull that can cause severe health issues and increase mortality rates, especially malignant types.
  • Early detection of brain tumors is critical for timely intervention and improved patient outcomes.
  • Computer-aided diagnostic (CAD) systems, integrated with artificial intelligence (AI), show significant promise for the early identification of brain tumors.

Purpose of the Study:

  • To review the existing literature on computer-aided diagnostic (CAD) systems for brain tumor detection.
  • To identify challenges and limitations associated with current CAD systems across various imaging modalities.
  • To outline the current requirements and future research prospects in AI-driven brain tumor diagnosis.

Main Methods:

  • Systematic review of 124 research articles published between 2000 and 2022.
  • Analysis of challenges faced by CAD systems based on different medical imaging modalities.
  • Identification of current needs and future trends in the field of AI for brain tumor detection.

Main Results:

  • The review identified key challenges in CAD systems for brain tumor detection, including data variability, algorithm generalizability, and interpretability.
  • Different imaging modalities present unique challenges for AI-based analysis.
  • There is a growing need for robust and reliable AI tools to support clinicians in early brain tumor diagnosis.

Conclusions:

  • AI and CAD systems are essential tools for the early detection of brain tumors, offering potential to improve diagnostic accuracy and speed.
  • Addressing the identified challenges is crucial for the advancement and clinical adoption of these technologies.
  • Future research should focus on developing more sophisticated AI algorithms, multimodal data integration, and clinical validation to enhance brain tumor diagnosis and patient care.