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Brain Imaging01:14

Brain Imaging

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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...
334

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Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease

Mustafa Kamal1, A Raghuvira Pratap2, Mohd Naved3

  • 1Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Dammam 32256, Saudi Arabia.

Computational Intelligence and Neuroscience
|April 14, 2022
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Summary
This summary is machine-generated.

This study introduces a machine learning framework for Alzheimer's disease detection using MRI scans. The system enhances image quality and extracts features for accurate classification, aiding in early diagnosis.

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Area of Science:

  • Medical imaging analysis
  • Computational neuroscience
  • Machine learning applications in healthcare

Background:

  • Alzheimer's disease (AD) is a neurodegenerative disorder characterized by abnormal protein aggregates in the brain.
  • The exact cause of AD remains elusive, necessitating advanced diagnostic tools.
  • Understanding brain nerve formation and alterations is crucial for developing effective treatments and prevention strategies.

Purpose of the Study:

  • To develop and evaluate a novel machine learning framework for the early detection of Alzheimer's disease.
  • To enhance the accuracy of Alzheimer's disease diagnosis through advanced image processing and classification techniques.
  • To investigate the efficacy of various machine learning classifiers in identifying Alzheimer's disease from MRI data.

Main Methods:

  • Utilized magnetic resonance imaging (MRI) datasets for Alzheimer's disease detection.
  • Implemented an image denoising technique using an adaptive mean filter to improve MRI quality.
  • Applied histogram equalization for image preprocessing and Haar wavelet transform for feature extraction.
  • Employed Least Squares Support Vector Machine with Radial Basis Function (LS-SVM-RBF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest classifiers for disease classification.

Main Results:

  • The proposed framework successfully processed MRI data, including denoising and feature extraction.
  • Comparative analysis of classification algorithms demonstrated varying levels of performance.
  • Evaluation metrics such as accuracy, sensitivity, specificity, precision, and recall were used to assess classifier effectiveness.

Conclusions:

  • The developed machine learning framework shows promise for accurate Alzheimer's disease detection using MRI.
  • Image preprocessing and feature extraction techniques significantly contribute to diagnostic performance.
  • Further research and validation are warranted to integrate this framework into clinical practice for early Alzheimer's diagnosis.