An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features
Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer disease is a major cause of dementia and eventually death all around the world. Early diagnosis of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2018-02-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2018.01008 |
| _version_ | 1811324634756284416 |
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| author | MALIK, F. FARHAN, S. FAHIEM, M. A. |
| author_facet | MALIK, F. FARHAN, S. FAHIEM, M. A. |
| author_sort | MALIK, F. |
| collection | DOAJ |
| description | Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons
resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer
disease is a major cause of dementia and eventually death all around the world. Early diagnosis
of the disease is crucial which can help the victims to maintain their level of independence for
comparatively longer time and live a best life possible. For early detection of Alzheimer's disease,
we are proposing a novel approach based on fusion of multiple types of features including hemodynamic,
volumetric and textural features of the brain. Our approach uses non-invasive fMRI with ensemble
of classifiers, for the classification of the normal controls and the Alzheimer patients. For
performance evaluation, ten-fold cross validation is used. Individual feature sets and fusion
of features have been investigated with ensemble classifiers for successful classification of
Alzheimer's patients from normal controls. It is observed that fusion of features resulted in
improved results for accuracy, specificity and sensitivity. |
| first_indexed | 2024-04-13T14:17:49Z |
| format | Article |
| id | doaj.art-9c8b0d63b4654ad0a784ae5d9f8edc65 |
| institution | Directory Open Access Journal |
| issn | 1582-7445 1844-7600 |
| language | English |
| last_indexed | 2024-04-13T14:17:49Z |
| publishDate | 2018-02-01 |
| publisher | Stefan cel Mare University of Suceava |
| record_format | Article |
| series | Advances in Electrical and Computer Engineering |
| spelling | doaj.art-9c8b0d63b4654ad0a784ae5d9f8edc652022-12-22T02:43:36ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002018-02-01181617010.4316/AECE.2018.01008An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic FeaturesMALIK, F.FARHAN, S.FAHIEM, M. A.Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer disease is a major cause of dementia and eventually death all around the world. Early diagnosis of the disease is crucial which can help the victims to maintain their level of independence for comparatively longer time and live a best life possible. For early detection of Alzheimer's disease, we are proposing a novel approach based on fusion of multiple types of features including hemodynamic, volumetric and textural features of the brain. Our approach uses non-invasive fMRI with ensemble of classifiers, for the classification of the normal controls and the Alzheimer patients. For performance evaluation, ten-fold cross validation is used. Individual feature sets and fusion of features have been investigated with ensemble classifiers for successful classification of Alzheimer's patients from normal controls. It is observed that fusion of features resulted in improved results for accuracy, specificity and sensitivity.http://dx.doi.org/10.4316/AECE.2018.01008biomedical image processingcomputer aided diagnosisfeature extractionimage classificationpattern recognition |
| spellingShingle | MALIK, F. FARHAN, S. FAHIEM, M. A. An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features Advances in Electrical and Computer Engineering biomedical image processing computer aided diagnosis feature extraction image classification pattern recognition |
| title | An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features |
| title_full | An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features |
| title_fullStr | An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features |
| title_full_unstemmed | An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features |
| title_short | An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features |
| title_sort | ensemble of classifiers based approach for prediction of alzheimer s disease using fmri images based on fusion of volumetric textural and hemodynamic features |
| topic | biomedical image processing computer aided diagnosis feature extraction image classification pattern recognition |
| url | http://dx.doi.org/10.4316/AECE.2018.01008 |
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