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Updated: Apr 27, 2026
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
Alzheimer's disease detection in brain magnetic resonance images using multiscale fractal analysis
Salim Lahmiri1, Mounir Boukadoum1
1Department of Computer Science, University of Quebec at Montreal, 201 President-Kennedy, Local PK-4150, Montreal, QC, Canada H2X 3Y7.
Abstract:
We present a new automated system for the detection of brain magnetic resonance images (MRI) affected by Alzheimer's disease (AD). The MRI is analyzed by means of multiscale analysis (MSA) to obtain its fractals at six different scales. The extracted fractals are used as features to differentiate healthy brain MRI from those of AD by a support vector machine (SVM) classifier. The result of classifying 93 brain MRIs consisting of 51 images of healthy brains and 42 of brains affected by AD, using leave-one-out cross-validation method, yielded 99.18% ± 0.01 classification accuracy, 100% sensitivity, and 98.20% ± 0.02 specificity. These results and a processing time of 5.64 seconds indicate that the proposed approach may be an efficient diagnostic aid for radiologists in the screening for AD.
Insights
A novel automated system uses multiscale analysis and support vector machines to detect Alzheimer's disease (AD) in brain MRIs. This efficient approach achieves high accuracy, aiding radiologists in AD screening.
Area of Science:
- Medical Imaging
- Neurology
- Artificial Intelligence
Background:
- Alzheimer's disease (AD) poses a significant diagnostic challenge.
- Accurate and early detection of AD is crucial for patient management.
- Current diagnostic methods can be time-consuming and invasive.
Purpose of the Study:
- To develop and validate an automated system for detecting Alzheimer's disease (AD) in brain Magnetic Resonance Images (MRI).
- To assess the efficacy of multiscale analysis (MSA) and Support Vector Machine (SVM) classification for AD detection.
- To establish the system's diagnostic performance and processing speed.
Main Methods:
- Brain MRIs were analyzed using multiscale analysis (MSA) to extract fractal features at six different scales.
- Extracted fractal features were used to train a Support Vector Machine (SVM) classifier.
- A dataset of 93 brain MRIs (51 healthy, 42 AD) was classified using leave-one-out cross-validation.
Main Results:
- The automated system achieved a classification accuracy of 99.18% ± 0.01.
- Sensitivity reached 100%, and specificity was 98.20% ± 0.02.
- The system demonstrated a rapid processing time of 5.64 seconds per MRI.
Conclusions:
- The proposed automated system demonstrates high accuracy and efficiency in detecting Alzheimer's disease from brain MRIs.
- The combination of MSA and SVM offers a promising tool for early AD screening.
- This approach has the potential to serve as an effective diagnostic aid for radiologists.
