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SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

Glenn Fung, Jonathan Stoeckel
2006 Knowledge and Information Systems  
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.  ...  In contrast with other linear hyperplane-based methods that perform simultaneous feature selection and classification, our proposed formulation incorporates proximity information about the features and  ...  Jacques Darcourt for their contributions to this work.  ... 
doi:10.1007/s10115-006-0043-5 fatcat:rad4mwskfff5lim5hsybjntkoi

Analysis of SPECT brain images for the diagnosis of Alzheimer's disease based on NMF for feature extraction

P. Padilla, J.M. Górriz, J. Ramírez, E.W. Lang, R. Chaves, F. Segovia, M. López, D. Salas-González, I. Álvarez
2010 Neuroscience Letters  
The SPECT database is analyzed by applying the Fisher discriminant ratio (FDR) for feature selection and NMF for feature extraction of relevant components of each subject.  ...  The proposed NMF + SVM method yields up to 94% classification accuracy, with high sensitivity and specificity values (upper than 90%), becoming an accurate method for SPECT image classification.  ...  Acknowledgments This work was partly supported by the MICINN of Spain under the PETRI DENCLASES (PET2006-0253), TEC2008-02113, NAPOLEON (TEC2007-68030-C02-01) and HD2008-0029 projects and the Consejería  ... 
doi:10.1016/j.neulet.2010.05.047 pmid:20641163 fatcat:jfduydptt5gixcosctk27rqk4i

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

R. Chaves, J. Ramírez, J.M. Górriz, M. López, D. Salas-Gonzalez, I. Álvarez, F. Segovia
2009 Neuroscience Letters  
., SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting, Neurosci.  ...  Lett. (2009), a b s t r a c t This letter shows a computer-aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) based on single photon emission computed tomography (SPECT  ...  Acknowledgments This work was partly supported by the MICINN of Spain under the PETRI DENCLASES (PET2006-0253), TEC2008-02113, NAPOLEON (TEC2007-68030-C02-01) and HD2008-0029 projects and the Consejería  ... 
doi:10.1016/j.neulet.2009.06.052 pmid:19549559 fatcat:po2qlhqz3veebgqbuc6yg2n3qm

Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines

Diego Salas-Gonzalez, Juan M. Górriz, Javier Ramírez, Miriam López, Ignacio A. Illan, Fermín Segovia, Carlos G. Puntonet, Manuel Gómez-Río
2009 Neuroscience Letters  
The proposed methodology is based on the calculation of the skewness for each m-by-m-by-m sliding block of the SPECT brain images.  ...  The center pixel in this m-by-m-by-m block is replaced by the skewness value to build a new 3-D brain image which is used for classification purposes.  ...  SVM is a powerful tool which has been recently used for classification of tomographic brain images [8, 10] .  ... 
doi:10.1016/j.neulet.2009.05.056 pmid:19477227 fatcat:maxyi67vxrag3mpq3zcjnnkjhq

A Survey: Early Detection of Alzheimer's Disease Using Different Techniques

Mareeswari S, Wiselin Jiji G
2015 International Journal on Computational Science & Applications  
In this paper, we have discussed various imaging modalities, feature selection and extraction, segmentation and classification techniques.  ...  Alzheimer's disease(AD) is a neurological disease. It affects memory. The livelihood of the people that are diagnosed with AD.  ...  Classifying Alzheimer's disease from Normal, MCI and AD by the use of SVM algorithm [2, 4, 6, 8] , KNN, ANN, SOM, PCNN and RF [3] algorithm.  ... 
doi:10.5121/ijcsa.2015.5103 fatcat:6tnxx5zv6ngzpfbin5qtzkdn2e

Alzheimer's Diagnosis Using Eigenbrains and Support Vector Machines [chapter]

I. Álvarez, J. M. Górriz, J. Ramírez, D. Salas-Gonzalez, M. López, F. Segovia, C. G. Puntonet, B. Prieto
2009 Lecture Notes in Computer Science  
The most relevant image features were selected under a PCA compression, which diagonalizes the covariance matrix, and the extracted information was used to train a SVM classifier which could classify new  ...  An accurate and early diagnosis of the Alzheimer's Disease (AD) is of fundamental importance for the patients medical treatment.  ...  Secondly, once a significant feature ensemble was selected, we built a SVM to manage the classification task.  ... 
doi:10.1007/978-3-642-02478-8_122 fatcat:ipk6vmhklzcdbddw74jf7cfge4

A COMPARATIVE SURVEY OF FEATURE EXTRACTION AND CLASSIFICATION TECHNIQUES FOR EARLY DIAGNOSIS OF ALZHIMER'S DISEASE

A. Sherin
2018 International Journal of Advanced Research in Computer Science  
In this paper, various feature extraction and classification approaches using the three imaging modalities, MRI, SPECT and PET along with their merits and demerits are discussed.  ...  In particular, this paper mainly focuses on feature extraction and classification approaches for early diagnosis of AD.  ...  [6] in 2007 have presented an approach for AD diagnosis using spatial information. This approach uses most relevant voxel and some areas as a feature vector for classification.  ... 
doi:10.26483/ijarcs.v9i2.5353 fatcat:is36dypr6feztkqt2h7eoils3u

Alzheimer\'s Disease Classification Using Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) Classifier

R. Sampath, A. Saradha
2015 Research Journal of Applied Sciences Engineering and Technology  
Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) classifier is used for prediction of Alzheimer's Disease (AD).  ...  In this study, Functional Magnetic Resonance Imaging (FMRI) offers considerable promise as a tool for detecting brain changes in Alzheimer disease pretentious patients.  ...  Dyrba et al. (2013) used Diffusion Tensor Imaging (DTI) as a biomarker, for diagnosis of alzheimer's disease.  ... 
doi:10.19026/rjaset.10.2550 fatcat:ix252pkkibbazdnvrpkt6c4g4q

A New Approach for Alzheimer's Disease Diagnosis by using Association Rule over PET Images

A. Veeramuthu, S. Meenakshi, P. S. Manjusha
2014 International Journal of Computer Applications  
A set of PET images is selected for the study.  ...  Alzheimer"s disease is usually diagnosed from patient history and clinical information. Finding appropriate technologies and early detection of AD is of fundamental importance for early treatments.  ...  The feature selection based on SVM for SPECT images [8] were also performed which uses linear programming formulation method for the classification of image using the spatial normalization information  ... 
doi:10.5120/15908-5009 fatcat:fpvdl7a6b5duzi2aiuzfe3z5xq

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach

Mutlu Mete, Unal Sakoglu, Jeffrey S. Spence, Michael D. Devous, Thomas S. Harris, Bryon Adinoff
2016 BMC Bioinformatics  
An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels.  ...  A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification.  ...  Note that in this study SVM was used as both classification tool and feature selection method.  ... 
doi:10.1186/s12859-016-1218-z pmid:27766943 pmcid:PMC5073995 fatcat:kguxm2qkxbarvgjbgrkrgdgpse

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology

Rosa Chaves, Javier Ramírez, Juan M Górriz, Ignacio A Illán, Manuel Gómez-Río, Cristobal Carnero
2012 BMC Medical Informatics and Decision Making  
Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis  ...  Background Alzheimer's Disease (AD) Alzheimer's Disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide [1].  ...  The PET data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904).  ... 
doi:10.1186/1472-6947-12-79 pmid:22849649 pmcid:PMC3512495 fatcat:6bx2cnwvsvetxn3l3z6fb4pqa4

Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease

Binny Naik, Ashir Mehta, Manan Shah
2020 Visual Computing for Industry, Biomedicine, and Art  
Alzheimer's disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown.  ...  Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.  ...  Acknowledgements The authors are grateful to Department of Computer Engineering, Indus University, Department of Chemical Engineering School of Technology, Pandit Deendayal Petroleum University for the  ... 
doi:10.1186/s42492-020-00062-w pmid:33151420 fatcat:cwmdiv6kmrfypnrqy2p3lkpe7m

NEUROIMAGING AND PATTERN RECOGNITION TECHNIQUES FOR AUTOMATIC DETECTION OF ALZHEIMER'S DISEASE: A REVIEW

Rupali Kamathe, Kalyani Joshi
2017 ICTACT Journal on Image and Video Processing  
A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD.  ...  Alzheimer's disease (AD) is the most common form of dementia with currently unavailable firm treatments that can stop or reverse the disease progression.  ...  Geetanjali Kadam, Dinanath Mangeshkar Hospital Pune for their valuable guidelines related to Alzheimer's disease required during this review work.  ... 
doi:10.21917/ijivp.2017.0219 fatcat:33i5bd3v7fbxhapbs35a2lipby

Automated identification of dementia using medical imaging: a survey from a pattern classification perspective

Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen
2015 Brain Informatics  
Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex-and ROI-based ones  ...  Our comparison shows that many algorithms can differentiate the Alzheimer's disease (AD) from elderly normal with a largely satisfying accuracy, whereas distinguishing the mild cognitive impairment from  ...  [93] combined the feature selection with classification using a Bayes classifier for the discrimination between AD and NC on MRI data and reported an accuracy of up to 92 %. Lopez et al.  ... 
doi:10.1007/s40708-015-0027-x pmid:27747596 pmcid:PMC4883162 fatcat:yefc226j4za75afkkkolg4m5n4

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification

J. Ramírez, J.M. Górriz, F. Segovia, R. Chaves, D. Salas-Gonzalez, M. López, I. Álvarez, P. Padilla
2010 Neuroscience Letters  
This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification.  ...  The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS.  ...  Acknowledgments This work was partly supported by the MICINN of Spain under the PETRI DENCLASES (PET2006-0253), TEC2008-02113, NAPOLEON (TEC2007-68030-C02-01) and HD2008-0029 projects and the Consejería  ... 
doi:10.1016/j.neulet.2010.01.056 pmid:20117177 fatcat:t66pd6rxt5hvlpxg27yvwvzvv4
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