title: Computer-aided diagnosis of Alzheimer's disease using support vector machines and classification trees.
abstract: This paper presents a computer-aided diagnosis technique for improving the accuracy of early diagnosis of Alzheimer-type dementia. The proposed methodology is based on the selection of voxels which present Welch's t-test between both classes, normal and Alzheimer images, greater than a given threshold. The mean and standard deviation of intensity values are calculated for selected voxels. They are chosen as feature vectors for two different classifiers: support vector machines with linear kernel and classification trees. The proposed methodology reaches greater than 95% accuracy in the classification task.
authors.affiliation: Department of Signal Theory Networking and Communications, ETSIIT, University of Granada, Spain. dsalas@ugr.es
authors.forename: D
authors.initials: D
authors.lastname: Salas-Gonzalez
authors.forename: J M
authors.initials: JM
authors.lastname: Górriz
authors.forename: J
authors.initials: J
authors.lastname: Ramírez
authors.forename: M
authors.initials: M
authors.lastname: López
authors.forename: I
authors.initials: I
authors.lastname: Alvarez
authors.forename: F
authors.initials: F
authors.lastname: Segovia
authors.forename: R
authors.initials: R
authors.lastname: Chaves
authors.forename: C G
authors.initials: CG
authors.lastname: Puntonet
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis* / Q000000981:diagnostic imaging; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D015899:Tomography, Emission-Computed, Single-Photon / Q000145:classification