Effective Detection of the Alzheimer Disease by Means of Coronal NMSE SVM Feature Classification

Authors:
Javier Ramírez;Rosa Chaves;Juan M. Górriz;Ignacio Álvarez;Diego Salas-Gonzalez;Míriam López;Fermín Segovia
Affiliations:
Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician's diagnosis. Conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. Currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on a feature extraction process based on the normalized mean square error (NMSE) features of several coronal slices of interest (SOI) and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach yielding a 97.5% AD diagnosis accuracy.