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Optimising multiple sclerosis detection: harnessing cutting-edge MRI image analysis for advanced industrial diagnosis

Author

Listed:
  • Mohammed Said Obeidat
  • Hussam A. Alshraideh
  • Abedallah A. Al Kader
  • Rabah M. Al Abdi
  • Morad Etier
  • Mohammad Hamasha

Abstract

Human brain disorders are those abnormal changes that occur around or inside brain parts. These disorders include infections, tumours, trauma, degeneration, structural defects, stroke, and autoimmune disorders. The devastating consequences of brain disorders on the lives of humans could be reduced by early diagnosis. The diagnosis of brain disorders consumes higher time and effort by physicians compared to computerised diagnosis techniques. Several computerised diagnosis algorithms have been developed to improve and optimise the diagnostic capabilities of physicians. Magnetic resonance imaging (MRI) is an effective tool used for brain disorders diagnosis. MRI detection of multiple sclerosis (MS) is extremely complicated due to several reasons, including the anatomical variability between patients, lesion location, and the variability in lesion's shape. This paper reviews several computerised algorithms used in diagnosing brain disorders, to present the most efficient techniques that reduce the physicians' diagnosis time and effort of MRI images, hence, starting MS treatment at earlier stages.

Suggested Citation

  • Mohammed Said Obeidat & Hussam A. Alshraideh & Abedallah A. Al Kader & Rabah M. Al Abdi & Morad Etier & Mohammad Hamasha, 2025. "Optimising multiple sclerosis detection: harnessing cutting-edge MRI image analysis for advanced industrial diagnosis," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 49(4), pages 506-519.
    • Handle: RePEc:ids:ijisen:v:49:y:2025:i:4:p:506-519
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