Article

SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information

Authors:

Jonathan Stoeckel,

Glenn FungAuthors Info & Claims

ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining

Pages 410 - 417

https://doi.org/10.1109/ICDM.2005.141

Published: 27 November 2005 Publication History

Abstract

Alzheimer's disease is the most frequent type of dementia for elderly patients. Due to aging populations the occurrence of this disease will increase in the next years. Early diagnosis is crucial to be able to develop more powerful treatments. Brain perfusion changes can be a marker for Alzheimer's disease. In this article we study the use of SPECT perfusion imaging for the diagnosis of Alzheimer's disease differentiating between images from healthy subjects and images from Alzheimer's disease patients. Our classification approach is based on a linear programming formulation similar to the 1-norm support vector machines. In contrastwith other linear hyperplane-based methods that perform simultaneous feature selection and classification, our proposed formulation incorporates proximity information about the features and generates a classifier that does not just select the most relevant voxels but the most relevant "areas" for classification resulting in more robust classifiersthat are better suitable for interpretation. This approach is compared with the classical Fisher linear discriminant (FLD) classifier as well as with statistical parametric mapping (SPM). We tested our method on data from four European institutions. Our method achieved sensitivity of 84.4% at 90.9% specificity, this is considerable better the human experts. Our method also outperformed the FLD and SPM techniques. We conclude that our approach has the potential to be a useful help for clinicians.

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Tanveer MRichhariya BKhan RRashid AKhanna PPrasad MLin C(2020)Machine Learning Techniques for the Diagnosis of Alzheimer’s DiseaseACM Transactions on Multimedia Computing, Communications, and Applications10.1145/334499816:1s(1-35)Online publication date: 17-Apr-2020
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SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information

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cover image Guide Proceedings

ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining

November 2005

837 pages

ISBN:0769522785

Publisher

IEEE Computer Society

United States

Publication History

Published: 27 November 2005

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Cited By

Kumar RTripathi T(2022)Performance Comparison of Machine Learning Algorithms for Dementia Progression DetectionInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.31255314:1(1-18)Online publication date: 25-Oct-2022
https://dl.acm.org/doi/10.4018/IJSSCI.312553
Tanveer MRichhariya BKhan RRashid AKhanna PPrasad MLin C(2020)Machine Learning Techniques for the Diagnosis of Alzheimer’s DiseaseACM Transactions on Multimedia Computing, Communications, and Applications10.1145/334499816:1s(1-35)Online publication date: 17-Apr-2020
https://dl.acm.org/doi/10.1145/3344998
Jiang ZSainju ATeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Hidden Markov Contour TreeProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330878(804-813)Online publication date: 25-Jul-2019
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Nozadi SKadoury S(2018)Classification of Alzheimer’s and MCI Patients from Semantically Parcelled PET ImagesJournal of Biomedical Imaging10.1155/2018/12474302018(1)Online publication date: 1-Mar-2018
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Ortiz AGórriz JRamírez JMartínez-Murcia F(2013)LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's diseasePattern Recognition Letters10.1016/j.patrec.2013.04.01434:14(1725-1733)Online publication date: 1-Oct-2013
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Zhang LZhou W(2013)A fast algorithm for kernel 1-norm support vector machinesKnowledge-Based Systems10.1016/j.knosys.2013.08.00852(223-235)Online publication date: 1-Nov-2013
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Li RHapfelmeier ASchmidt JPerneczky RDrzezga AKurz AKramer S(2011)A case study of stacked multi-view learning in dementia researchProceedings of the 13th conference on Artificial intelligence in medicine10.5555/2040981.2040992(60-69)Online publication date: 2-Jul-2011
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Xia YZhang ZWen LDong PFeng D(2011)GA and adaboost-based feature selection and combination for automated identification of dementia using FDG-PET imagingProceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering10.1007/978-3-642-31919-8_17(128-135)Online publication date: 23-Oct-2011
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Nguyen Mde la Torre F(2010)Optimal feature selection for support vector machinesPattern Recognition10.1016/j.patcog.2009.09.00343:3(584-591)Online publication date: 1-Mar-2010
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Segovia FRamírez JGórriz JChaves RSalas-Gonzalez DLópez MÁlvarez IPadilla PPuntonet C(2010)Partial least squares for feature extraction of SPECT imagesProceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I10.1007/978-3-642-13769-3_58(476-483)Online publication date: 23-Jun-2010
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