Classification of Alzheimer's Disease using MRI Images and Machine Learning Techniques

Articles in Press

Document Type : Original Article

Authors

1 Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz,, Iran

2 Faculty of Biomedical Engineering Sahand University of Technology

3 Professor/Faculty of Biomedical Engineering, Sahand University of Technology

4 Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran

Abstract

Alzheimer's disease is a neurodegenerative disorder characterized by a gradual decline in cognitive function, including memory loss and impaired thinking. Common symptoms include progressive memory decline, personality changes, difficulty with reasoning and decision-making, and decreased ability to perform daily tasks. Magnetic Resonance Imaging (MRI) can provide high-resolution images of brain structures, which can aid in identifying signs of brain disorders, including Alzheimer's. This study proposes a method for detecting Alzheimer's disease using MRI images from the Kaggle Alzheimer's disease database, which comprises four stages of the disease: healthy, very mild demented, mild demented, and moderate demented. The proposed method involves segmenting MRI images using Fuzzy C-means clustering and particle swarm optimization, followed by feature extraction using Histogram Oriented Gradients (HOG) and Local Binary Pattern (LBP). To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to generate a balanced dataset. Feature selection is then performed using the ReliefF algorithm. The evaluation results demonstrate that the proposed method achieves high accuracy (99.87%), sensitivity (99.74%), and specificity (99.91%) in classifying different stages of Alzheimer's disease, showing promise for early detection and diagnosis.

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Tabriz Journal of Electrical Engineering

Articles in Press, Corrected Proof
Available Online from 05 March 2025

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