SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting

Chaves, R.; Ramı́rez, Javier; Górriz, J. M.; López, Manuel López; Salas-González, D.; Álvarez, Isaac; Segovia, F.

doi:10.1016/j.neulet.2009.06.052

Cited by 123 publications

(65 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, it is not yet clear in which brain areas this hypoperfusion is most evidenced and thus which one would be the most accurate one for AD diagnosis. Temporoparietal region has been considered practical for the early detection of AD [94], but its sensitivity and specificity is still questioned [91]. Some suggest that posterior cingulate gyri and precunei regions could be more useful [95] while medial temporal lobe (MTL) and hippocampus regions cannot be analysed due to the depth to which they are located [96].…”

Section: Single Photon Emission Computed Tomography (Spect)mentioning

confidence: 99%

“…CAD systems have been developed using SPECT images and machine-learning techniques [84,86,94,97,98]. Lopez et al [84] have been able to distinguish AD patients of Alzheimer's Disease neuroimaging initiative (ADNI) database [99] from CTLs with 96.7% accuracy, using PCA based features of preselected slices of interest and an SVM classifier with a quadratic kernel.…”

Section: Single Photon Emission Computed Tomography (Spect)mentioning

confidence: 99%

See 1 more Smart Citation

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Alberdi

Aztiria

Basarab

2016

Artificial Intelligence in Medicine

167

View full text Add to dashboard Cite

Section: Single Photon Emission Computed Tomography (Spect)mentioning

confidence: 99%

Section: Single Photon Emission Computed Tomography (Spect)mentioning

confidence: 99%

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Alberdi

Aztiria

Basarab

2016

Artificial Intelligence in Medicine

167

View full text Add to dashboard Cite

“…Furthermore, this intensity normalization procedure has been successfully applied in other recent works. [24][25][26][27] …”

Section: Iib Image Registrationmentioning

confidence: 99%

Feature selection using factor analysis for Alzheimer's diagnosis using PET images

et al. 2010

Self Cite

View full text Add to dashboard Cite

Purpose: This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease ͑AD͒. Two hundred and ten 18 F-FDG PET images from the ADNI initiative ͓52 normal controls ͑NC͒, 114 mild cognitive impairment ͑MCI͒, and 53 AD subjects͔ are studied. Methods: The proposed methodology is based on the selection of voxels of interest using the t-test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel. Results: An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC-MCI and NC-MCI,AD, respectively, are obtained using SVM with linear kernel. Conclusions:Results are compared to the voxel-as-features and a PCA-based approach and the proposed methodology achieves better classification performance.

show abstract

“…SVM classifier is shown to be an effective classification tool of AD and MCI (Chaves, et al, 2009b;Kloppel, et al, 2008;Lopez, et al, 2009).The SVM as implemented in this study uses a sequential minimal optimization (SMO) scheme to implement a L1 softmargin SVM classifier. SVM maps the original features via a kernel function to constructs a maximum margin classifier in a high dimensional feature space.…”

Section: Svm Based Classification Experimentsmentioning

confidence: 99%

“…The common objective of these techniques is to project the data into a decisional space where the total variance or variance related to class separation is maximized. Then linear or nonlinear using specific classifiers are then determined to delineate the populations or groups under study (Chaves, et al, 2009a;López, et al, 2009;Magnin, et al, 2009b;Zhou, et al, 2014d).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Neuroimaging Approach for the Prediction and Analysis of Alzheimer’s Disease and its Prodromal Stages

Zhou¹

View full text Add to dashboard Cite

selected for each combination model were used to construct the classification space using support vector machines. To combine MRI and PET, orthogonal partial least squares to latent structures is used as a multivariate analysis to discriminate between AD, early and late MCI (EMCI and LMCI) from cognitively normal (CN)s. In addition, this dissertation proposes a new effective mean indicator (EMI) method for distinguishing stages of AD from CN. EMI utilizes the mean of specific top-ranked measures, determined by incremental error analysis, to achieve optimal separation of AD and CN.viii For AD vs. CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores were found to improve classification accuracy by 8.2% and 12% for aMCI vs. CN and naMCI vs. CN, respectively. Brain atrophy was almost evenly seen on both sides of the brain for AD subjects, which was different from right side dominance for aMCI and left side dominance for naMCI. Findings suggest that subcortical volume need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or the mean thickness. Furthermore, MRI and PET had comparable predictive power in separating AD from CN. For the EMCI prediction, cortical thickness was found to be the best predictor, even better than using all features together. Validation with an external test set demonstrated that best of feature-selected models for the LMCI group was able to classify 83% of the LMCI subjects. The EMI-based method achieved an accuracy of 92.7% using only MRI features. The performance of the EMI-based method along with its simplicity suggests great potential for its use in clinical trials.

show abstract

SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting

Cited by 123 publications

References 13 publications

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

Feature selection using factor analysis for Alzheimer's diagnosis using PET images

An Integrated Neuroimaging Approach for the Prediction and Analysis of Alzheimer’s Disease and its Prodromal Stages

Contact Info

Product

Resources

About