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Arabi, Esraa M., Ahmed, Khaled S., Mohra, Ashraf S.. (1401). Advanced Diagnostic Technique for Alzheimer’s Disease using MRI Top-Ranked Volume and Surface-based Features. سامانه مدیریت نشریات علمی, 12(6), 569-582. doi: 10.31661/jbpe.v0i0.2112-1440
Esraa M. Arabi; Khaled S. Ahmed; Ashraf S. Mohra. "Advanced Diagnostic Technique for Alzheimer’s Disease using MRI Top-Ranked Volume and Surface-based Features". سامانه مدیریت نشریات علمی, 12, 6, 1401, 569-582. doi: 10.31661/jbpe.v0i0.2112-1440
Arabi, Esraa M., Ahmed, Khaled S., Mohra, Ashraf S.. (1401). 'Advanced Diagnostic Technique for Alzheimer’s Disease using MRI Top-Ranked Volume and Surface-based Features', سامانه مدیریت نشریات علمی, 12(6), pp. 569-582. doi: 10.31661/jbpe.v0i0.2112-1440
Arabi, Esraa M., Ahmed, Khaled S., Mohra, Ashraf S.. Advanced Diagnostic Technique for Alzheimer’s Disease using MRI Top-Ranked Volume and Surface-based Features. سامانه مدیریت نشریات علمی, 1401; 12(6): 569-582. doi: 10.31661/jbpe.v0i0.2112-1440

Advanced Diagnostic Technique for Alzheimer’s Disease using MRI Top-Ranked Volume and Surface-based Features

Journal of Biomedical Physics and Engineering
دوره 12، شماره 6، بهمن و اسفند 2022، صفحه 569-582    اصل مقاله (1.47 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2112-1440
نویسندگان
Esraa M. Arabi1؛ Khaled S. Ahmed* 2؛ Ashraf S. Mohra2
1MSc, Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt
2PhD, Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha, Egypt
چکیده
Background: Alzheimer’s disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer‘s patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses. 
Objective: This study aimed to develop an efficient automated method to differentiate Alzheimer’s patients from normal elderly and present the essential features with accurate Alzheimer’s diagnosis.
Material and Methods: In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm. 
Results: The LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively.  
Conclusion: The proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time.
کلیدواژه‌ها
Hippocampus؛ Amygdala؛ Cortical Thickness؛ Gyrification Index؛ Sulcal Depth؛ Alzheimer Disease؛ Relief Algorithm
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