Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease

López, Manuel López; Ramı́rez, Javier; Górriz, Juan Manuel; Álvarez, Isaac; Salas-González, D.; Segovia, F.; Chaves, R.; Padilla, Pablo; Gómez-Río, Manuel

doi:10.1016/j.neucom.2010.06.025

Cited by 151 publications

(69 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dehghan [83] improved these results combining both FDG and PiB PET scans, and using PCA and SVM algorithms for feature extraction and classification, they achieved 94.12% of accuracy distinguishing AD from healthy controls and 82.05% in the case of MCI and controls. A group of investigators of the University of Granada has published several important works proposing automatic PET based AD diagnosis tools [84][85][86], reporting high accuracies of up to 98.3% distinguishing AD patients and healthy controls, 77.47% separating CTLs from both AD and MCI patients and 68.79% in classifying MCI patients and controls.…”

Section: Pet Scansmentioning

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%

“…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. Ramirez et al [97] used SPECT images of 52 subjects, and a methodology based on first and second order image parameter selection and SVM classification.…”

Section: Single Photon Emission Computed Tomography (Spect)mentioning

confidence: 99%

See 2 more Smart Citations

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: Pet Scansmentioning

confidence: 99%

Section: Single Photon Emission Computed Tomography (Spect)mentioning

confidence: 99%

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

“…Numerous studies have reported methods for distinguishing patients with AD from healthy cases [5][6][7][8], but few studies have described methods for distinguishing patients with AD from other types of dementia [9]. To our knowledge, no study to date has described the classification of AD versus PD using SPECT imaging.…”

Section: Introductionmentioning

confidence: 99%

Classification of Alzheimer’s disease and Parkinson’s disease using a support vector machine and probabilistic outputs

Hatabu

Harada

Takahashi³

et al. 2017

CBIJ

View full text Add to dashboard Cite

Alzheimer's disease (AD) and Parkinson's disease (PD) are both prominent central nervous system diseases that are frequently diagnosed and studied using brain single-photon emission computed tomography (SPECT). Owing to divergent clinical features, AD and PD are often considered distinct diseases; however, it is difficult to distinguish AD from PD on SPECT. Tools for objectively analyzing differences between AD and PD on SPECT images are not currently available. To construct a model for discriminating AD from PD in Japanese patients, we used a support vector machine (SVM) and SPECT images acquired at two different time points after radiotracer injection to extract the determinant regions for classification. We assessed SPECT images from 68 Japanese patients with AD or PD. After pre-processing noise voxels, a non-linear SVM classification with Gaussian kernels was adopted to construct the predictive model. The best SVM model was highly accurate for distinguishing AD from PD. The accuracy of this model was 98.1% for leave-one-out cross-validation and 78.6% for the test set. Our data showed that the temporal, sub-lobar, parietal, limbic, and frontal areas exhibited decreased regional cerebral blood flow in AD; whereas the frontal, anterior, parietal, and occipital areas exhibited decreased regional cerebral blood flow in PD. Here, we present a useful SVM model for classifying AD versus PD using SPECT images and show the utility of two-time-point SPECT imaging for AD/PD discrimination.

show abstract

“…Alzheimer's clinical researchers are currently seeking the assistance of large-scale information technology resources to enable them to study masses of neuroimaging data being accumulated across the older patient community so that early onset indicators such as cortical thinning can be studied [3] [4]. Rapid advances in neuroimaging technologies such as PET, SPECT, MR spectroscopy, DTI and fMRI have offered a new vision into the pathophysiology of AD [5] and, consequently, new increasingly powerful data analysis methods have been developed [6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent grid enabled services for neuroimaging analysis

et al. 2013

View full text Add to dashboard Cite

This paper reports our work in the context of the neuGRID project in the development of intelligent services for a robust and efficient Neuroimaging analysis environment. neuGRID is an EC-funded project driven by the needs of the Alzheimer's disease research community that aims to facilitate the collection and archiving of large amounts of imaging data coupled with a set of services and algorithms. By taking Alzheimer's disease as an exemplar, the neuGRID project has developed a set of intelligent services and a Grid infrastructure to enable the European neuroscience community to carry out research required for the study of degenerative brain diseases. We have investigated the use of machine learning approaches, especially evolutionary multi-objective meta-heuristics for optimising scientific analysis on distributed infrastructures. The salient features of the services and the functionality of a planning and execution architecture based on an evolutionary multi-objective meta-heuristics to achieve analysis efficiency are presented. We also describe implementation details of the services that will form an intelligent analysis environment and present results on the optimisation that has been achieved as a result of this investigation.

show abstract

Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease

Cited by 151 publications

References 25 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

Classification of Alzheimer’s disease and Parkinson’s disease using a support vector machine and probabilistic outputs

Intelligent grid enabled services for neuroimaging analysis

Contact Info

Product

Resources

About