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An 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.
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biomedical image processing, computer aided diagnosis, feature extraction, image classification, pattern recognition
alzheimer(51), disease(38), imaging(12), functional(12), fmri(12), brain(11), diagnosis(10), dementia(8), classification(8), neuroimage(7)
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About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01008
Web of Science Accession Number: 000426449500008
SCOPUS ID: 85043280771
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.
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 Convolutional Neural Network Based Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease: A Technique using Hippocampus Extracted from MRI, MUKHTAR, G., FARHAN, S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.02013 [CrossRef] [Full text]
 Deep learning for Alzheimer's disease diagnosis: A survey, Khojaste-Sarakhsi, M., Haghighi, Seyedhamidreza Shahabi, Ghomi, S.M.T. Fatemi, Marchiori, Elena, Artificial Intelligence in Medicine, ISSN 0933-3657, Issue , 2022.
Digital Object Identifier: 10.1016/j.artmed.2022.102332 [CrossRef]
 Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images, Uteng, Stig, Johansen, Thomas Haugland, Zaballos, Jose Ignacio, Ortega, Samuel, Holmström, Lasse, Callico, Gustavo M., Fabelo, Himar, Godtliebsen, Fred, Applied Sciences, ISSN 2076-3417, Issue 7, Volume 10, 2020.
Digital Object Identifier: 10.3390/app10072298 [CrossRef]
 Diagnosis of Alzheimer's Disease from Brain Magnetic Resonance Imaging Images using Deep Learning Algorithms, SUGANTHE, R. C., LATHA, R. S., GEETHA, M., SREEKANTH, G. R., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 20, 2020.
Digital Object Identifier: 10.4316/AECE.2020.03007 [CrossRef] [Full text]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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