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Accuracy of classification by using SVM classifier with Polynomial kernel function. 

Accuracy of classification by using SVM classifier with Polynomial kernel function. 

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Table 1 : Subject Cohort Normal MCI Alzheimer 
Table 2 : Accuracy of classification by using SVM classifier with...
Table 3 :: Accuracy of classification by using SVM classifier with...
Table 5 : Comparison of classification accuracy (in %) reported by...
Diagnosing Alzheimer disease based on resting-state FMRI images and latent low rank representation
Conference Paper
Full-text available
  • Feb 2017
In recent years, there has been a growing trend in diagnosing neurologic and psychiatric diseases by functional magnetic resonance imaging (FMRI). The goal of this research is diagnosing Alzheimer disease based on resting-state FMRI (rs-FMRI). In this work, the images of rs-FMRI related to 10 Alzheimer patients, 10 early Mild Cognitive Impairment (...
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... It is worth mentioning that by omitting global signal, no important information was added to the data and it is safer to keep it since it is the mean of all signals in the brain, and also considering 7 regions in brain which play the most important role regarding the Alzheimer's disease (helps to increase the accuracy up to around 15%). In the end, it is worth mentioning that, the best method to parcellate the brain to use ROIs based on probabilistic atlases of macroscopic anatomy or probabilistic atlases that are available as part of the SPM Anatomy Toolbox or FSL [25,26]. ...
Alzheimer Disease Diagnosis from fMRI images Based on Latent Low Rank Features and Support Vector Machine (SVM)
Article
Full-text available
  • Aug 2021
  • CURR SIGNAL TRANSD T
Purpose: In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly used as a noninvasive and practical method in different areas of neuroscience and psychology for recognizing brain’s mechanism as well as diagnosing neurological diseases. In this work, we use rs-fMRI data for diagnosing Alzheimer disease. Design/methodology/approach: To do that, by using the rs-fMRI of a patient, we computed the time series of some anatomical regions and then applied the Latent Low Rank Representation method to extract suitable features. Next, based on the extracted features we apply a Support Vector Machine (SVM) classifier to determine whether the patient belongs to healthy category, mild stage of the disease or Alzheimer stage. Findings: The obtained classification accuracy for the proposed method is more than 97.5%. Originality/value: We performed different experiments on a database of rs-fMRI data containing the images of 43 healthy subjects, 36 mild cognitive impairment patients and 32 Alzheimer patients and the obtained results demonstrated that the best performance is achieved when the SVM with Gaussian kernel and the features of only 7 regions were used.
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