A Review on Image Classification Techniques for MRI Brain Stroke Lesion

Authors

  • Norhashimah Mohd Saad Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Malaysia
  • Abdul Rahim Abdullah Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Izzatul Husna Azman Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia
  • Nor Shahirah Mohd Noor Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

DOI:

https://doi.org/10.37934/araset.40.2.6273

Keywords:

Brain stroke lesion, diffusion weighted imaging, classification

Abstract

A stroke, a fatal brain disorder with systemic consequences, emphasizes the crucial need of timely treatment. Recent studies emphasize the "time is brain" notion, which states that starting therapy within six hours improves outcomes. Manual stroke diagnosis by neuroradiologists is commonplace, but subjective and time-consuming. This study examines and views methods for classifying stroke lesions, with an emphasis on Machine Learning and Deep Learning for brain scan processing. Deep Learning thrives on complicated data but necessitates many resources. Simpler architecture is desired. The study's goal is to improve stroke classification, allowing for faster, more precise medical choices and treatment. This research has the potential to lead to enhanced healthcare solutions powered by intelligent systems.

Author Biographies

Norhashimah Mohd Saad, Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Malaysia

norhashimah@utem.edu.my

Ts. Dr. Norhashimah Mohd Saad

Senior Lecturer - Digital Image and Signal Processing, Computer Vision and Medical Imaging

Faculty of Electrical and Electronic Engineering Technology,
Universiti Teknikal Malaysia Melaka, Malaysia

Abdul Rahim Abdullah, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

abdulr@utem.edu.my

PM. Ir. Dr.Abdul Rahim Abdullah

Associate Professor - Advance Digital Signal Processing, Rehabilitation Engineering, Assistive Technology and Power Electronics and Drive

Faculty of Electrical Engineering,
Universiti Teknikal Malaysia Melaka, Malaysia

 

Izzatul Husna Azman, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

husnacino@gmail.com

Nor Shahirah Mohd Noor, Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

cyramdnoor@gmail.com

Nor Shahirah Mohd Noor

Master Student - Stroke medical image segmentation and classification using machine learning and deep learning

Faculty of Electronics and Computer Engineering,
Universiti Teknikal Malaysia Melaka, Malaysia

Downloads

Published

2024-02-28

How to Cite

Norhashimah Mohd Saad, Abdul Rahim Abdullah, Izzatul Husna Azman, & Nor Shahirah Mohd Noor. (2024). A Review on Image Classification Techniques for MRI Brain Stroke Lesion. Journal of Advanced Research in Applied Sciences and Engineering Technology, 40(2), 62–73. https://doi.org/10.37934/araset.40.2.6273

Issue

Vol. 40 No. 2: October (2024)

Section

Articles

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