Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer's disease. (July 2019)

• Record Type: Journal Article
• Authors: Lahmiri, Salim; Shmuel, Amir
• Is Part Of: Biomedical signal processing and control. Volume 52(2019)
• Page Start: 414
• Page End: 419
• Publication Date: 2019-07
• Subjects: Alzheimer's disease -- ADNI -- Cortical thickness -- Gyrification index -- Fractals -- ADAS cognitive test -- Machine learning -- Classification -- Support vector machine; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.08.009
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Early detection of Alzheimer's disease (AD) using structural magnetic resonance images is essential for early treatment that can slow the progression of the disease. Therefore, there is a need for accurate computer-aided-diagnosis (CAD) systems for detecting AD. The purpose of this work is to evaluate the degree to which specific features - including fractals obtained from MRI-based surfaces of the cerebral cortex, cortical thickness, gyrification index and the Alzheimer's disease assessment scale (ADAS) cognitive test scores - are informative for classifying AD patients and healthy control subjects using several machine learning classifiers. Our results show that a Support Vector Machine (SVM) trained with cortical thickness, gyrification index and ADAS cognitive test scores distinguishes between AD and healthy control subjects better than other machine learning methods and other feature combinations. This specific CAD system achieved ideal accuracy and outperformed recently proposed systems.