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Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation

Sabina Tangaro, Nicola Amoroso, Massimo Brescia, Stefano Cavuoti, Andrea Chincarini, Rosangela Errico, Paolo Inglese, Giuseppe Longo, Rosalia Maglietta, Andrea Tateo, Giuseppe Riccio, Roberto Bellotti
2015 Computational and Mathematical Methods in Medicine  
In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods  ...  , respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested  ...  Acknowledgments The authors would like to thank the anonymous referee for extremely valuable comments and suggestions. Nicola Amoroso, Rosangela Errico, Paolo Inglese, and Andrea Tateo  ... 
doi:10.1155/2015/814104 pmid:26089977 pmcid:PMC4450305 fatcat:lxoqyreapvehrgi4vwvy2g4aqy

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Yingying Guo, Jianfeng Qiu, Weizhao Lu
2020 Brain Sciences  
Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid  ...  could be used by machine learning algorithms for the classification of schizophrenia.  ...  Acknowledgments: We thank the Center for Biomedical Research Excellence (COBRE) for sharing their data. We also thank all participants in this study.  ... 
doi:10.3390/brainsci10080562 pmid:32824267 fatcat:kzeepsbtlbag7n3wbl4e6qpgei

Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network [article]

Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, the Alzheimer's Disease Neuroimaging Initiative
2019 arXiv   pre-print
Different from the traditional machine learning algorithms, this method does not require manually extracted features, and utilizes the stateof-art 3D image-processing CNNs to learn features for the diagnosis  ...  In the last decade, studies on AD diagnosis attached great significance to artificial intelligence (AI)-based diagnostic algorithms.  ...  Through the experiments, we proved that segmentation is not necessary in CNN, which is different from traditional machine learning based methods.  ... 
arXiv:1902.09904v1 fatcat:pzzhu2mdfzhgbldhqwtofaz3sq

Hippocampus segmentation in magnetic resonance images of Alzheimer's patients using Deep machine learning [article]

Hadi Varmazyar, Hossein Yousefi-Banaem, Saber Malekzadeh, Nahideh Gharehaghaji
2021 arXiv   pre-print
Objective: The aim of this study was the segmentation of the hippocampus in magnetic resonance (MR) images of Alzheimers patients using deep machine learning method.  ...  Methods: U-Net architecture of convolutional neural network was proposed to segment the hippocampus in the real MRI data.  ...  CSF around the hippocampus. 13 Conclusion In this study, we proposed a method for segmenting the hippocampal area of the brain on MRI T1w images of the Alzheimer's patients.  ... 
arXiv:2106.06743v1 fatcat:mynvqcyusjgvzlsvt6j4boy2ym

A Survey of Different Machine Learning Models for Alzheimer Disease Prediction

Ragavamsi Davuluri
2020 International Journal of Emerging Trends in Engineering Research  
Machine learning model is one of the best disease prediction framework in various medical disease prediction processes.  ...  Component selection plays a significant role in improving the performance of these programs. Therefore, various forms of feature selection techniques are analyzed in this survey article.  ...  TRADITIONAL FEATURE SELECTION MODELS Feature selection is one of the significant steps in data mining and machine learning.  ... 
doi:10.30534/ijeter/2020/73872020 fatcat:6z5ke75e4zfenbthmmj3all32e

Neuroimage Biomarker Identification of the Conversion of Mild Cognitive Impairment to Alzheimer's Disease

Te-Han Kung, Tzu-Cheng Chao, Yi-Ru Xie, Ming-Chyi Pai, Yu-Min Kuo, Gwo Giun Chris Lee
2021 Frontiers in Neuroscience  
Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings.  ...  The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and  ...  Kewei Chen and his team for providing us with the earlier ADNI dataset for studying.  ... 
doi:10.3389/fnins.2021.584641 pmid:33746695 pmcid:PMC7968420 fatcat:rszgokhyijebfle4v7udltziry

Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry

Lauge Sørensen, Christian Igel, Akshay Pai, Ioana Balas, Cecilie Anker, Martin Lillholm, Mads Nielsen
2017 NeuroImage: Clinical  
Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry Sørensen,  ...  Acknowledgments This work was supported in part by the Danish National Advanced Technology Foundation (project 034-2011-5, "Early MRI diagnosis of Alzheimer's Disease") and in part by Eurostars (project  ...  8234, "MR Brain Image Quantification in Dementia").  ... 
doi:10.1016/j.nicl.2016.11.025 pmid:28119818 pmcid:PMC5237821 fatcat:3c2rqkbpfffa5ebnlo3wfbdbta

Correction: Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy

Fariborz Mahmoudi, Kost Elisevich, Hassan Bagher-Ebadian, Mohammad-Reza Nazem-Zadeh, Esmaeil Davoodi-Bojd, Jason M. Schwalb, Manpreet Kaur, Hamid Soltanian-Zadeh
2018 PLoS ONE  
Special thanks to Saeed Shokri, Harrini Vijay, and Mario Dervishi for their invaluable help in data processing and Rushna Ali for editing the manuscript.  ...  Acknowledgments This work was supported in part by NIH grant R01EB013227.  ...  Kim et al [42] proposed a multispectral and multimodal approach based on high-resolution T1-and T2-weighted MRI with hippocampal subfield segmentations, to carry out lateralization efforts in mTLE patients  ... 
doi:10.1371/journal.pone.0209866 pmid:30571727 pmcid:PMC6301553 fatcat:woi3y4ib6reh5l5wzgftpvkka4

Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm

Qun Shang, Qi Zhang, Xiao Liu, Lingchen Zhu, Deepika Koundal
2022 Computational and Mathematical Methods in Medicine  
This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer's disease (AD), which was based on hippocampal volume changes in  ...  The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established.  ...  Conclusion Based on machine learning method, early AD was predicted by hippocampal subregion volume changes.  ... 
doi:10.1155/2022/3144035 pmid:35572832 pmcid:PMC9106502 fatcat:ieapm6n2mzgpdkcy5zjqgijqr4

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

Xiaomu Tang, Jie Liu
2021 Annals of Palliative Medicine  
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.  ...  Of the three machine learning algorithms, RF was better than the SVM and DT at predicting different MRI features. The accuracy of RF, SVM, and DT was 73.8%, 60.7%, and 59.5%, respectively.  ...  According to the prediction results of CN-EMCI in Figure 2A , in relation to the three machine learning algorithms, the prediction effect of the RF for different MRI features was better than that of the  ... 
doi:10.21037/apm-21-2013 pmid:34628897 fatcat:4pbntorc6banzdgnmzavl25xry

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  
Neuroimaging and computer-aided diagnosis techniques are used for classification of AD by physicians in the early stage. Most of the previous machine learning techniques work on handpicked features.  ...  In the recent days, deep learning has been applied for many medical image applications.  ...  Hippocampal volume is verified patchwise [22] ; patch-based image features are selected by professional and medical experts with knowledge in medical segmentation.  ... 
doi:10.1016/j.trci.2019.10.001 pmid:31921971 pmcid:PMC6944731 fatcat:ts46h5xoqzc4zcevblqzatsj5a

Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy

Fariborz Mahmoudi, Kost Elisevich, Hassan Bagher-Ebadian, Mohammad-Reza Nazem-Zadeh, Esmaeil Davoodi-Bojd, Jason M. Schwalb, Manpreet Kaur, Hamid Soltanian-Zadeh, Boris C Bernhardt
2018 PLoS ONE  
., feature extraction, feature selection, machine learning classifiers) were applied to provide measures of the relative contributions of structures and their correlations with one another.  ...  Purpose This study systematically investigates the predictive power of volumetric imaging feature sets extracted from select neuroanatomical sites in lateralizing the epileptogenic focus in mesial temporal  ...  Special thanks to Saeed Shokri, Harrini Vijay, and Mario Dervishi for their invaluable help in data processing and Rushna Ali for editing the manuscript.  ... 
doi:10.1371/journal.pone.0199137 pmid:30067753 pmcid:PMC6070173 fatcat:gvmkgx3dafbv7b5hmlvt6hkunq

An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures

Nikhil Bhagwat, Jon Pipitone, Aristotle N. Voineskos, M. Mallar Chakravarty, Alzheimer's Disease Neuroimaging Initiative
2019 Journal of Psychiatry & Neuroscience  
The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages.  ...  as the Alzheimer's Disease Assessment Scale or the Mini Mental State Examination) using MRI data has received less attention.  ...  This ability of the APANN model to predict ADAS-13 and MMSE and scores based on structural MRI features may prove to be valuable from a clinical perspective in helping to build prognostic tools.  ... 
doi:10.1503/jpn.180016 pmid:30720260 pmcid:PMC6606432 fatcat:7tgzchgdcbfwxi33abq7bvyv5m

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI

Michael Rebsamen, Piotr Radojewski, Richard McKinley, Mauricio Reyes, Roland Wiest, Christian Rummel
2022 Frontiers in Neurology  
For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature.  ...  Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM).ResultsDeep  ...  Next, we identified the most important shape feature of the hippocampus using a machine-learning classifier and subsequently examined this feature for its ability to support the radiological assessment  ... 
doi:10.3389/fneur.2022.812432 pmid:35250818 pmcid:PMC8894898 fatcat:pefls3t7kngfvesejxxrxzjpri

A Review Article on Brain Tumor Detection and Optimization using Hybrid Classification Algorithm

Nitesh Yadav
2021 International Journal for Research in Applied Science and Engineering Technology  
In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and timeconsuming task.  ...  Keywords: Brain tumor, data mining techniques, filtering techniques, MRI, classifiers, feature selection.  ...  THRESHOLD BASED SEGMENTATION Threshold is one of the aged procedures for image segmentation.  ... 
doi:10.22214/ijraset.2021.38903 fatcat:hjeg36lrcjeolke3ryukofhjny
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