title: Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.
abstract: Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.
authors: 1 Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors: Laila
authors: L
authors: Khedher
authors: 1 Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors: Ignacio A
authors: IA
authors: Illán
authors: 1 Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors: Juan M
authors: JM
authors: Górriz
authors: 1 Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors: Javier
authors: J
authors: Ramírez
authors: 1 Department of Signal Theory, Networking and Communications, University of Granada, Granada 18071, Spain.
authors: Abdelbasset
authors: A
authors: Brahim
authors: 2 Department of Scientific Computing, Florida State University, Tallahassee, FL, USA.
authors: Anke
authors: A
authors: Meyer-Baese
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008609:Mental Status Schedule; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Alzheimer’s disease; MRI; computer-aided diagnosis; independent component analysis; support vector machines