Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer’s with Visual Support
| Autor: | Ignacio A. Illán, Anke Meyer-Baese, Abdelbasset Brahim, Laila Khedher, Juan Manuel Górriz, Javier Ramírez |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Male
Support Vector Machine Computer Networks and Communications Computer science Feature extraction CAD 02 engineering and technology Machine learning computer.software_genre Sensitivity and Specificity 03 medical and health sciences 0302 clinical medicine Neuroimaging Alzheimer Disease Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Cognitive Dysfunction Aged Interpretability business.industry Supervised learning Brain General Medicine Magnetic Resonance Imaging Independent component analysis Support vector machine Computer-aided diagnosis Female 020201 artificial intelligence & image processing Artificial intelligence Mental Status Schedule business computer Algorithms 030217 neurology & neurosurgery |
| Zdroj: | International Journal of Neural Systems. 27:1650050 |
| ISSN: | 1793-6462 0129-0657 |
| DOI: | 10.1142/s0129065716500507 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|