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A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

Alexander Luke Spedding, Giuseppe Di Fatta, Mario Cannataro
2015 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment  ...  A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier.  ...  ACKNOWLEDGMENT Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).  ... 
doi:10.1109/bibm.2015.7359909 dblp:conf/bibm/SpeddingFC15 fatcat:37hbazobmzbqpekuxtv4ikbm2u

Classification of Neurodegenerative Disease Stages using Ensemble Machine Learning Classifiers

M. Rohini, D. Surendran
2019 Procedia Computer Science  
It also classifies the features if the subjects with Mild Cognitive impairment (MCI) and Pre-Mild Cognitive Impairment (Pre-MCI)has the likelihood to develop Alzheimer's disease.  ...  It also classifies the features if the subjects with Mild Cognitive impairment (MCI) and Pre-Mild Cognitive Impairment (Pre-MCI)has the likelihood to develop Alzheimer's disease.  ...  Many studies were performed on expert system solutions for Alzheimer's disease and Mild Cognitive Impairment conversions.  ... 
doi:10.1016/j.procs.2020.01.071 fatcat:u2hpn2beebadvb2upcsqxj4iau

Predictive models for mild cognitive impairment to Alzheimer's disease conversion

Konstantina Skolariki, GraciellaMuniz Terrera, SamuelO Danso
2021 Neural Regeneration Research  
of MRI data in Alzheimer’s disease and mild cognitive impairment.  ...  in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI.  ... 
doi:10.4103/1673-5374.306071 pmid:33510068 fatcat:26a3aud6nvfxfc5pi64aoi32ya

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia

Nitsa J. Herzog, George D. Magoulas
2021 Sensors  
It uses features of brain asymmetries, extracted from MRI of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification  ...  Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer's Disease (AD)), and can help  ...  Acknowledgments: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department  ... 
doi:10.3390/s21030778 pmid:33498908 fatcat:7wvbs7voovcytnexkezntwr3lm

Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging

Kwangsik Nho, Li Shen, Sungeun Kim, Shannon L Risacher, John D West, Tatiana Foroud, Clifford R Jack, Michael W Weiner, Andrew J Saykin
2010 AMIA Annual Symposium Proceedings  
Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD).  ...  Classification was performed using support vector machines (SVMs) together with a SVM-based feature selection method, which selected a set of most discriminating predictors for optimizing prediction accuracy  ...  ABSTRACT Mild Cognitive Impairment (MCI) is thought to be a precursor to the development of early Alzheimer's disease (AD).  ... 
pmid:21347037 pmcid:PMC3041374 fatcat:g32gap4rnndpbdx7lh7xz7alom

A list of publications describing new supervised learning pipelines to predict clinical variables from neuroimaging data in Alzheimer's disease

Alex F Mendelson
2016 Figshare  
This is a list of publications describing new classification and regression methods to predict clinical variables relevant to Alzheimer's disease using neurological images.  ...  It is intended as a companion document for my thesis.  ...  Demirel, A. D. N. Initiative, et al. Probability distri- bution function-based classification of structural MRI for the detection of Alzheimer's disease.  ... 
doi:10.6084/m9.figshare.3435752 fatcat:mdtfbkinjzfezcg6qhhwx43s4e

Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease using structural MRI analysis

V.P. Subramanyam Rallabandi, Ketki Tulpule, Mahanandeeshwar Gattu
2020 Informatics in Medicine Unlocked  
In this paper, we developed an automated machine learning method for classifying cognitively normal aging, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease individuals  ...  Materials and Methods: In this study, a total of 1167 whole-brain magnetic resonance imaging scans of individuals who are cognitively normal aging controls, early mild cognitive impairment, late mild cognitive  ...  ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California.Title: Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer's disease  ... 
doi:10.1016/j.imu.2020.100305 fatcat:hn3nyde2cvhqfmcrxceuckambu

Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI [article]

Jafar Zamani, Ali Sadr, Amir-Homayoun Javadi
2021 bioRxiv   pre-print
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD).  ...  These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network  ...  Acknowledgements The authors would like to thank Oliver Herdson for his comments and proofreading the manuscript. Authors Contribution JZ and AHJ conceived the study. JZ extracted the data.  ... 
doi:10.1101/2021.03.04.433989 fatcat:k3vv2wezbnay5fibpl4q32hfka

Survey on Identification of Alzheimer Disease Using Magnetic Resonance Imaging (MRI) Images

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
For the purpose of this work, there had been a new survey that had been made for the identification of a new case of Alzheimer's disease by means of using the MRI images.  ...  Alzheimer's disease (AD) is a neuro-degenerative disorder which is characterised functional and cognitive deficits that take place progressively.  ...  Future work to focus on optimizing selection of features and classifiers to improve the prediction.  ... 
doi:10.35940/ijitee.k2487.0981119 fatcat:qjvljb74onamfb33n4i2fpbhbi

Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer's Case Study

Georgiana Ingrid Stoleru, Adrian Iftene
2022 Mathematics  
In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI).  ...  Alzheimer's Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process.  ...  Mild Cognitive Impairment (LMCI), AD and CN.  ... 
doi:10.3390/math10101767 fatcat:hkfp72kyhzdvbbluu6ylfrwtzy

Alzheimer Disease Detection Techniques and Methods: A Review

Sitara Afzal, Muazzam Maqsood, Umair Khan, Irfan Mehmood, Hina Nawaz, Farhan Aadil, Oh-Young Song, Yunyoung Nam
2021 International Journal of Interactive Multimedia and Artificial Intelligence  
Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification.  ...  In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis  ...  Acknowledgment "This work was supported by the Soonchunhyang University Research Fund. "  ... 
doi:10.9781/ijimai.2021.04.005 fatcat:yklogr5wefei7e247dmjdrosum

Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation

Shaik Basheera, M Satya Sai Ram
2019 Alzheimer s & Dementia Translational Research & Clinical Interventions  
Predicting AD from mild cognitive impairment (MCI) and cognitive normal (CN) has become popular.  ...  In recent times, accurate and early diagnosis of Alzheimer's disease (AD) plays a vital role in patient care and further treatment.  ...  Table 1 1 Demographic representation of MRI images Abbreviations: AD, Alzheimer's disease; ADNI, Alzheimer's Disease Neuroimaging Initiative; CN, cognitive normal; MCI, mild cognitive impairment; MRI,  ... 
doi:10.1016/j.trci.2019.10.001 pmid:31921971 pmcid:PMC6944731 fatcat:ts46h5xoqzc4zcevblqzatsj5a

Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review

Sergio Grueso, Raquel Viejo-Sobera
2021 Alzheimer's Research & Therapy  
Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer's disease dementia  ...  Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare  ...  The authors refer to literature but do not develop their mathematical notation or architecture High (2) No information about the model  ... 
doi:10.1186/s13195-021-00900-w pmid:34583745 fatcat:al4ho7vq7bdwtgwoklq6o5x43e

Alzheimer's Disease Diagnosis using Deep Learning Techniques

2020 International Journal of Engineering and Advanced Technology  
A thorough review of various algorithms of deep learning for diagnosis of Alzheimer's disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it  ...  Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI  ...  With the aim of the diagnosis of Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI) and classification method among healthy subjects and the two groups.  ... 
doi:10.35940/ijeat.c5345.029320 fatcat:7e2uqgeh3venbbnyhn23fr763e

Comparing different algorithms for the course of Alzheimer's disease using machine learning

Xiaomu Tang, Jie Liu
2021 Annals of Palliative Medicine  
This paper discusses the characteristic indexes of brain magnetic resonance imaging (MRI) in mild cognitive impairment (MCI) and AD.  ...  It applies the MRI characteristic indexes in machine learning to classify and predict the course of AD to select the best model for classification and prediction auxiliary diagnosis of AD.  ...  Table 2 2 Comparison of the five MRI features MRI, magnetic resonance imaging; CN, cognitive normal; EMCI, early mild cognitive impairment; LMCI, late mild cognitive impairment; AD, Alzheimer's disease  ... 
doi:10.21037/apm-21-2013 pmid:34628897 fatcat:4pbntorc6banzdgnmzavl25xry
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