Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure...

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Vydané v:	Journal of medical systems Ročník 43; číslo 9; s. 302 - 14
Hlavní autori:	Acharya, U. Rajendra, Fernandes, Steven Lawrence, WeiKoh, Joel En, Ciaccio, Edward J., Fabell, Mohd Kamil Mohd, Tanik, U. John, Rajinikanth, V., Yeong, Chai Hong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.09.2019 Springer Nature B.V
Predmet:	Abnormalities Alzheimer Disease - classification Alzheimer Disease - diagnostic imaging Alzheimer's disease Brain Brain - pathology Classification Cognitive ability Comparative analysis Diagnosis Diagnosis, Computer-Assisted - methods Feature extraction Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image acquisition Image detection Image Interpretation, Computer-Assisted - methods Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Medicine Medicine & Public Health Neuroimaging NMR Nuclear magnetic resonance Pattern Recognition, Automated - methods Recent Advances in Deep Learning for Biomedical Signal Processing Sensitivity Series (mathematics) Statistics for Life Sciences Performance evaluation Feature extraction KNN classifier Brain MRI Alzheimer’s disease
ISSN:	0148-5598, 1573-689X, 1573-689X
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Abstract	The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.
AbstractList	The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database. The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database. The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.
ArticleNumber	302
Author	Fernandes, Steven Lawrence Rajinikanth, V. Ciaccio, Edward J. Fabell, Mohd Kamil Mohd Tanik, U. John Yeong, Chai Hong Acharya, U. Rajendra WeiKoh, Joel En
Author_xml	– sequence: 1 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences – sequence: 2 givenname: Steven Lawrence surname: Fernandes fullname: Fernandes, Steven Lawrence organization: Department of Electronics and Communication Engineering, Sahyadri College of Engineering & Management – sequence: 3 givenname: Joel En surname: WeiKoh fullname: WeiKoh, Joel En organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic – sequence: 4 givenname: Edward J. surname: Ciaccio fullname: Ciaccio, Edward J. organization: Department of Medicine, Columbia University – sequence: 5 givenname: Mohd Kamil Mohd surname: Fabell fullname: Fabell, Mohd Kamil Mohd organization: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya – sequence: 6 givenname: U. John surname: Tanik fullname: Tanik, U. John organization: Department of Computer Science and Information Systems, Texas A&M University-Commerce – sequence: 7 givenname: V. orcidid: 0000-0003-3897-4460 surname: Rajinikanth fullname: Rajinikanth, V. email: v.rajinikanth@ieee.org organization: Department of Electronics and Instrumentation, St. Joseph’s College of Engineering – sequence: 8 givenname: Chai Hong surname: Yeong fullname: Yeong, Chai Hong organization: School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31396722$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1016/j.neuroscience.2015.08.013 10.1016/j.patrec.2017.05.028 10.1016/j.nic.2011.11.002 10.1159/000438457 10.1016/j.jbi.2011.01.001 10.1038/s41598-018-27337-w 10.1109/CETIC4.2018.8530910 10.1016/j.neuroimage.2009.11.046 10.1016/j.future.2018.03.023 10.1016/j.neuroimage.2013.06.033 10.7785/tcrt.2012.500214 10.1007/s00415-017-8395-1 10.1016/j.ins.2017.08.050 10.1016/j.neuroimage.2016.12.026 10.1016/j.dsp.2009.07.002 10.1016/j.compbiomed.2017.02.011 10.1016/j.jalz.2018.06.2498 10.1001/jama.2015.4669 10.1109/TIP.2010.2041410 10.1109/MSP.2005.1550194 10.1016/j.bspc.2006.05.002 10.3233/JAD-2012-129016 10.1109/SPICES.2015.7091517 10.1016/j.bspc.2018.10.010 10.1016/j.jneumeth.2017.12.010 10.1016/j.jalz.2016.02.010 10.1016/j.cmpb.2016.10.007 10.1016/j.neuroimage.2012.04.056 10.1007/978-981-10-3274-5_1 10.1109/TIP.2013.2244223 10.1016/j.neuroimage.2018.08.040 10.3233/JAD-150848 10.2528/PIER12061410 10.1002/msj.20279 10.1016/j.jneumeth.2018.01.003 10.1007/s11042-016-4222-4 10.1016/j.jneumeth.2011.01.027 10.1016/S0140-6736(11)60582-5 10.2214/ajr.182.1.1820003 10.3390/app6060169 10.1016/j.compmedimag.2018.08.002 10.1016/j.phpro.2012.03.133 10.1142/S0129065712500025 10.1016/j.knosys.2017.06.003 10.1016/j.future.2018.08.008 10.3233/JAD-170069 10.7717/peerj.1251 10.1016/j.ins.2018.01.051 10.1016/S1672-0229(08)60011-X 10.1007/s12652-018-0854-8 10.1371/journal.pone.0177044 10.1212/WNL.0b013e3182166e96 10.1016/j.neuroimage.2010.03.051 10.2528/PIER13121310 10.1186/alzrt276 10.1016/j.jocs.2017.02.006 10.1007/s10586-017-0977-2
ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
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References	BeheshtiIDemirelHMatsudaHClassification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithmComput. Biol. Med.20178310911910.1016/j.compbiomed.2017.02.01128260614 RajinikanthVSatapathySCFernandesSLNachiappanSEntropy based segmentation of tumor from brain MR images–a study with teaching learning based optimizationPattern Recogn. Lett.201794879510.1016/j.patrec.2017.05.028 ZhangYLenanWAn MR brain images classifier via principal component analysis and kernel support vector machineProg. Electromagn. Res.201213036938810.2528/PIER12061410 RaghavendraUBhatNSGudigarAAcharyaURAutomated system for the detection of thoracolumbar fractures using a CNN architectureFutur. Gener. Comput. Syst.20188518418910.1016/j.future.2018.03.023 HeadEPowellDGoldBTSchmittFAAlzheimer's disease in down syndromeEuropean Journal of Neurodegenerative Diseases201213353364 ZhangYWangSDongZClassification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision treeProg. Electromagn. Res.201414417118410.2528/PIER13121310 CalsolaroVEdisonPNeuroinflammation in Alzheimer's disease: Current evidence and future directionsAlzheimer's and Dementia201612671973210.1016/j.jalz.2016.02.01027179961 MalikGARobertsonNPTreatments in Alzheimer’s diseaseJ. Neurol.201726424164181:STN:280:DC%2BC1c7ptVSitw%3D%3D10.1007/s00415-017-8395-1281200415306064 NorfrayJFProvenzaleJMAlzheimer’s disease: neuropathologic findings and recent advances in imagingAm. J. Roentgenol.2004182131310.2214/ajr.182.1.1820003 ZhangYWangSSuiYYangMLiuBChengHMultivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimizationJ. Alzheimers Dis.20186585586910.3233/JAD-17006928731432 KrishnanKGVanathiPTAbinayaRTexture classification using Shearlet transform energy featuresCommunications in Computer and Information Science201667931310.1007/978-981-10-3274-5_1 ChaplotSPatnaikLMJagannathanNRClassification of magnetic resonance brain images using wavelets as input to support vector machine and neural networkBiomedical Signal Processing and Control200611869210.1016/j.bspc.2006.05.002 WangS-HZhangYLiY-JJiaW-JLiuF-YYangM-MSingle slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimizationMultimed. Tools Appl.2016779103931041710.1007/s11042-016-4222-4 Raja, N. S. M., Fernandes, S. L., Dey, N., Satapathy, S. C., and Rajinikanth, V., Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J. Ambient. Intell. Humaniz. Comput.:1–12, 2018. https://doi.org/10.1007/s12652-018-0854-8. SotensenLNielsenMEnsemble support vector machine classification of dementia using structural MRI and mini-mental state examinationJ. Neurosci. Methods2018302667410.1016/j.jneumeth.2018.01.003 HaaksmaMiriam L.VilelaLara R.MarengoniAlessandraCalderón-LarrañagaAmaiaLeoutsakosJeannie-Marie S.Olde RikkertMarcel G. M.MelisRené J. F.Comorbidity and progression of late onset Alzheimer’s disease: A systematic reviewPLOS ONE2017125e017704410.1371/journal.pone.0177044284722005417646 EditorialThe three stages of Alzheimer's diseaseLancet20113779776146510.1016/S0140-6736(11)60582-5 JhaDKwonG-RContourlet-based feature extraction for computer-aided classification of Alzheimer’s diseaseAlzheimers and Dementia2018147147310.1016/j.jalz.2018.06.2498 GorjiHTHaddadniaJA novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRINeuroscience20153053613711:CAS:528:DC%2BC2MXhtlCru73L10.1016/j.neuroscience.2015.08.01326265552 WangSZhangYLiuGPhillipsPYuanT-FDetection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imagingJ. Alzheimers Dis.2016502332481:CAS:528:DC%2BC28XnvVGltA%3D%3D10.3233/JAD-15084826682696 El-DahshanE-SAHosnyTSalemA-BMHybrid intelligent techniques for MRI brain images classificationDigital Signal Processing20102043344110.1016/j.dsp.2009.07.002 ZhangYDongZPhillipsPWangSJiGYangJDetection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learningFront. Comput. Neurosci.2015966260827134451357 SelesnickIWBaraniukRGKingsburyNCThe dual-tree complex wavelet transformIEEE Signal Process. Mag.200522612315110.1109/MSP.2005.1550194 LiuXLocally linear embedding (LLE) for MRI based Alzheimer's disease classificationNeuroImage20138314815710.1016/j.neuroimage.2013.06.03323792982 GudigarARaghavendraUSanTRCiaccioEJAcharyaURApplication of multiresolution analysis for automated detection of brain abnormality using MR images: A comparative studyFutur. Gener. Comput. Syst.20199035936710.1016/j.future.2018.08.008 ChandraBGuptaMAn efficient statistical feature selection approach for classification of gene expression dataJ. Biomed. Inform.20114445295351:STN:280:DC%2BC3Mjgt1GqsQ%3D%3D10.1016/j.jbi.2011.01.00121241823 BraskieMNTogaAWThompsonPMRecent advances in imaging alzheimer’s diseaseJ. Alzheimers Dis.2013331S313S32710.3233/JAD-2012-129016226728804110110 ZhouNWangLA Modified T-test Feature Selection Method and Its Application on the Hap Map Genotype DataGenomics Proteomics Bioinformatics200753–424224910.1016/S1672-0229(08)60011-X18267305 WangS-HDuSZhangYPhillipsPWuL-NChenX-QAlzheimer’s disease detection by pseudo Zernike moment and linear regression classificationCNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders)2017161115 LimW-QThe discrete Shearlet transform: a new directional transform and compactly supported Shearlet framesIEEE Trans. Image Process.20101951166118010.1109/TIP.2010.204141020106737 OssenkoppeleRPrevalence of Amyloid PET Positivity in Dementia Syndromes A Meta-analysisJ. Am. Med. Assoc.2015313191939194910.1001/jama.2015.4669 GaoXWHuiRTianZClassification of CT brain images based on deep learning networksComput. Methods Prog. Biomed.2017138495610.1016/j.cmpb.2016.10.007 World Health Organization (2018) The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death Johnson, K.A., and Becker J.A. The whole brain atlas. Available from Harvard Medical School, USA http://www.med.harvard.edu/aanlib TanJHAcharyaURBhandarySVChuaKCSivaprasadSSegmentation of optic disc, fovea and retinal vasculature using a single convolutional neural networkJ. Comput. Sci.2017207079 NeugroschlJWangSAlzheimer’s disease: Diagnosis and treatment across the spectrum of disease severityMt Sinai J. Med.201178459661210.1002/msj.20279217487483315348 WestmanEMuehlboeckJ-SSimmonsACombining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversionNeuroImage20126222923810.1016/j.neuroimage.2012.04.05622580170 SankariZAdeliHProbabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherenceJ. Neurosci. Methods2011197116517010.1016/j.jneumeth.2011.01.02721310177 HettKTaV-TManjónJVCoupéPAdaptive fusion of texture-based grading for Alzheimer's disease classificationComput. Med. Imaging Graph.20187081610.1016/j.compmedimag.2018.08.00230273832 AcharyaURFaustOSreeSVMolinariFGarberoglioRSuriJSCost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ AlgorithmsTechnology in Cancer Research & treatment20111043713801:STN:280:DC%2BC3MnlslKjsg%3D%3D10.7785/tcrt.2012.500214 LimW-QNonseparable Shearlet transformIEEE Trans. Image Process.20132252056206510.1109/TIP.2013.224422323372085 PichMImaging as a biomarker in drug discovery for Alzheimer’s disease: is MRI a suitable technology?Alzheimers Res. Ther.20146511:CAS:528:DC%2BC2cXhvVGnsLbN10.1186/alzrt276 StonningtonCMPredicting clinical scores from magnetic resonance scans in Alzheimer's diseaseNeuroImage2010541405141310.1016/j.neuroimage.2010.03.051 AcharyaURFujitaHLihOSAdamMTanJHChuaCKAutomated detection of coronary artery disease using different durations of ECG segments with convolutional neural networkKnowl.-Based Syst.2017132627110.1016/j.knosys.2017.06.003 DimitriadisSILiparasDTsolakiMNRandom forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and Alzheimer’s disease patients: From the alzheimer’s disease neuroimaging initiative (ADNI) databaseJ. Neurosci. Methods201830214231:STN:280:DC%2BC1MzktlSjtw%3D%3D10.1016/j.jneumeth.2017.12.01029269320 WhalleyLJSpatial distribution and secular trends in the epidemiology of Alzheimer’s diseaseNeuroimaging Clin. N. Am.201222111010.1016/j.nic.2011.11.00222284729 DickersonBStoubTShahRSperlingRKillianyRAlbertMAlzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adultsNeurology201176139514021:CAS:528:DC%2BC3MXkvValtbw%3D10.1212/WNL.0b013e3182166e96214903233087406 AyadiWElhamziWCharfiIAtriMA hybrid feature extraction approach for brain MRI classification based on Bag-of-wordsBiomedical Signal Processing and Control20194814415210.1016/j.bspc.2018.10.010 C. Patil, M. Mathura, S. Madhumitha, S. S. David, M. Fernandes, A. Venugopal, et al., Using Image Processing on MRI Scans, in Signal Processing, Informatics, IEEE International Conference on Communication and Energy Systems (SPICES), pp. 1–5, 2015, 2015. https://doi.org/10.1109/SPICES.2015.7091517. PlantCTeipelSJOswaldABöhmCMeindlTMourao-MirandaJAutomated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's diseaseNeuroimage20105016217410.1016/j.neuroimage.2009.11.046199619382838472 ZhuYHuangCAn improved median filtering algorithm for image noise reductionPhys. Procedia20122560961610.1016/j.phpro.2012.03.133 AcharyaU. 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References_xml	– reference: C. Patil, M. Mathura, S. Madhumitha, S. S. David, M. Fernandes, A. Venugopal, et al., Using Image Processing on MRI Scans, in Signal Processing, Informatics, IEEE International Conference on Communication and Energy Systems (SPICES), pp. 1–5, 2015, 2015. https://doi.org/10.1109/SPICES.2015.7091517. – reference: SotensenLNielsenMEnsemble support vector machine classification of dementia using structural MRI and mini-mental state examinationJ. Neurosci. Methods2018302667410.1016/j.jneumeth.2018.01.003 – reference: TanJHAcharyaURBhandarySVChuaKCSivaprasadSSegmentation of optic disc, fovea and retinal vasculature using a single convolutional neural networkJ. Comput. Sci.2017207079 – reference: World Health Organization (2018) The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death – reference: AcharyaU. RajendraSudarshanVidya K.AdeliHojjatSanthoshJayasreeKohJoel E.W.PuthankattiSubha D.AdeliAmirA Novel Depression Diagnosis Index Using Nonlinear Features in EEG SignalsEuropean Neurology2015741-2798310.1159/00043845726303033 – reference: HaaksmaMiriam L.VilelaLara R.MarengoniAlessandraCalderón-LarrañagaAmaiaLeoutsakosJeannie-Marie S.Olde RikkertMarcel G. M.MelisRené J. F.Comorbidity and progression of late onset Alzheimer’s disease: A systematic reviewPLOS ONE2017125e017704410.1371/journal.pone.0177044284722005417646 – reference: SelesnickIWBaraniukRGKingsburyNCThe dual-tree complex wavelet transformIEEE Signal Process. Mag.200522612315110.1109/MSP.2005.1550194 – reference: ZhangYWangSSunPPhillipsPPathological brain detection based on wavelet entropy and Hu moment invariantsBiomed. Mater. Eng.201526S1283S129026405888 – reference: ZhangYWangSSuiYYangMLiuBChengHMultivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimizationJ. Alzheimers Dis.20186585586910.3233/JAD-17006928731432 – reference: OssenkoppeleRPrevalence of Amyloid PET Positivity in Dementia Syndromes A Meta-analysisJ. Am. Med. Assoc.2015313191939194910.1001/jama.2015.4669 – reference: Johnson, K.A., and Becker J.A. The whole brain atlas. Available from Harvard Medical School, USA http://www.med.harvard.edu/aanlib/ – reference: TanJHAutomated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural networkInf. Sci.2017420667610.1016/j.ins.2017.08.050 – reference: SankariZAdeliHProbabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherenceJ. Neurosci. Methods2011197116517010.1016/j.jneumeth.2011.01.02721310177 – reference: ChandraBGuptaMAn efficient statistical feature selection approach for classification of gene expression dataJ. Biomed. Inform.20114445295351:STN:280:DC%2BC3Mjgt1GqsQ%3D%3D10.1016/j.jbi.2011.01.00121241823 – reference: WangS-HZhangYLiY-JJiaW-JLiuF-YYangM-MSingle slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimizationMultimed. Tools Appl.2016779103931041710.1007/s11042-016-4222-4 – reference: El-DahshanE-SAHosnyTSalemA-BMHybrid intelligent techniques for MRI brain images classificationDigital Signal Processing20102043344110.1016/j.dsp.2009.07.002 – reference: DimitriadisSILiparasDTsolakiMNRandom forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and Alzheimer’s disease patients: From the alzheimer’s disease neuroimaging initiative (ADNI) databaseJ. Neurosci. Methods201830214231:STN:280:DC%2BC1MzktlSjtw%3D%3D10.1016/j.jneumeth.2017.12.01029269320 – reference: StonningtonCMPredicting clinical scores from magnetic resonance scans in Alzheimer's diseaseNeuroImage2010541405141310.1016/j.neuroimage.2010.03.051 – reference: HettKTaV-TManjónJVCoupéPAdaptive fusion of texture-based grading for Alzheimer's disease classificationComput. Med. Imaging Graph.20187081610.1016/j.compmedimag.2018.08.00230273832 – reference: CalsolaroVEdisonPNeuroinflammation in Alzheimer's disease: Current evidence and future directionsAlzheimer's and Dementia201612671973210.1016/j.jalz.2016.02.01027179961 – reference: WangTQiuRGYuMPredictive modeling of the progression of Alzheimer’s disease with recurrent neural networksSci. Rep.2018891611:CAS:528:DC%2BC1cXhvVGit7rI10.1038/s41598-018-27337-w299077476003986 – reference: GaoXWHuiRTianZClassification of CT brain images based on deep learning networksComput. Methods Prog. Biomed.2017138495610.1016/j.cmpb.2016.10.007 – reference: WestmanEMuehlboeckJ-SSimmonsACombining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversionNeuroImage20126222923810.1016/j.neuroimage.2012.04.05622580170 – reference: CasanovaRUsing high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databasesNeuroImage201818340141110.1016/j.neuroimage.2018.08.04030130645 – reference: GorjiHTHaddadniaJA novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRINeuroscience20153053613711:CAS:528:DC%2BC2MXhtlCru73L10.1016/j.neuroscience.2015.08.01326265552 – reference: ZhangYDongZPhillipsPWangSJiGYangJDetection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learningFront. Comput. Neurosci.2015966260827134451357 – reference: WangS-HDuSZhangYPhillipsPWuL-NChenX-QAlzheimer’s disease detection by pseudo Zernike moment and linear regression classificationCNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders)2017161115 – reference: ZhouNWangLA Modified T-test Feature Selection Method and Its Application on the Hap Map Genotype DataGenomics Proteomics Bioinformatics200753–424224910.1016/S1672-0229(08)60011-X18267305 – reference: PlantCTeipelSJOswaldABöhmCMeindlTMourao-MirandaJAutomated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's diseaseNeuroimage20105016217410.1016/j.neuroimage.2009.11.046199619382838472 – reference: RajinikanthVSatapathySCFernandesSLNachiappanSEntropy based segmentation of tumor from brain MR images–a study with teaching learning based optimizationPattern Recogn. Lett.201794879510.1016/j.patrec.2017.05.028 – reference: ZhangYLenanWAn MR brain images classifier via principal component analysis and kernel support vector machineProg. Electromagn. Res.201213036938810.2528/PIER12061410 – reference: ZhaoYRaichleMEWenJBenzingerTLFaganAMHassenstabJIn vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imagingNeuroimage201714829630410.1016/j.neuroimage.2016.12.02627989773 – reference: RaghavendraUBhatNSGudigarAAcharyaURAutomated system for the detection of thoracolumbar fractures using a CNN architectureFutur. Gener. Comput. Syst.20188518418910.1016/j.future.2018.03.023 – reference: VaratharajanRManogaranGPriyanMSundarasekarRWearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithmClust. Comput.201721168169010.1007/s10586-017-0977-2 – reference: EditorialThe three stages of Alzheimer's diseaseLancet20113779776146510.1016/S0140-6736(11)60582-5 – reference: LimW-QThe discrete Shearlet transform: a new directional transform and compactly supported Shearlet framesIEEE Trans. Image Process.20101951166118010.1109/TIP.2010.204141020106737 – reference: Raja, N. S. M., Fernandes, S. L., Dey, N., Satapathy, S. C., and Rajinikanth, V., Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J. Ambient. Intell. Humaniz. Comput.:1–12, 2018. https://doi.org/10.1007/s12652-018-0854-8. – reference: NeugroschlJWangSAlzheimer’s disease: Diagnosis and treatment across the spectrum of disease severityMt Sinai J. Med.201178459661210.1002/msj.20279217487483315348 – reference: KrishnanKGVanathiPTAbinayaRTexture classification using Shearlet transform energy featuresCommunications in Computer and Information Science201667931310.1007/978-981-10-3274-5_1 – reference: BraskieMNTogaAWThompsonPMRecent advances in imaging alzheimer’s diseaseJ. Alzheimers Dis.2013331S313S32710.3233/JAD-2012-129016226728804110110 – reference: WhalleyLJSpatial distribution and secular trends in the epidemiology of Alzheimer’s diseaseNeuroimaging Clin. N. Am.201222111010.1016/j.nic.2011.11.00222284729 – reference: NorfrayJFProvenzaleJMAlzheimer’s disease: neuropathologic findings and recent advances in imagingAm. J. Roentgenol.2004182131310.2214/ajr.182.1.1820003 – reference: HeadEPowellDGoldBTSchmittFAAlzheimer's disease in down syndromeEuropean Journal of Neurodegenerative Diseases201213353364 – reference: RaghavendraUDeep convolution neural network for accurate diagnosis of glaucoma using digital fundus imagesInf. Sci.2018441414910.1016/j.ins.2018.01.051 – reference: BeheshtiIDemirelHMatsudaHClassification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithmComput. Biol. Med.20178310911910.1016/j.compbiomed.2017.02.01128260614 – reference: PichMImaging as a biomarker in drug discovery for Alzheimer’s disease: is MRI a suitable technology?Alzheimers Res. Ther.20146511:CAS:528:DC%2BC2cXhvVGnsLbN10.1186/alzrt276 – reference: AcharyaURSreeSVAngPCAYantiRSuriJSApplication of non-linear and wavelet based features for the automated identification of epileptic EEG signalsInt. J. Neural Syst.20122202125000210.1142/S012906571250002523627588 – reference: AcharyaURFaustOSreeSVMolinariFGarberoglioRSuriJSCost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ AlgorithmsTechnology in Cancer Research & treatment20111043713801:STN:280:DC%2BC3MnlslKjsg%3D%3D10.7785/tcrt.2012.500214 – reference: GudigarARaghavendraUSanTRCiaccioEJAcharyaURApplication of multiresolution analysis for automated detection of brain abnormality using MR images: A comparative studyFutur. Gener. Comput. Syst.20199035936710.1016/j.future.2018.08.008 – reference: WangSDual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain DetectionAppl. Sci.20166616910.3390/app6060169 – reference: LiuXLocally linear embedding (LLE) for MRI based Alzheimer's disease classificationNeuroImage20138314815710.1016/j.neuroimage.2013.06.03323792982 – reference: KaurAKaurPA comparative study of various exudate segmentation techniques for diagnosis of diabetic retinopathyInternational Journal of Current Engineering and Technology201646142146 – reference: ZhangYWangSDongZClassification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision treeProg. Electromagn. Res.201414417118410.2528/PIER13121310 – reference: AcharyaURFujitaHLihOSAdamMTanJHChuaCKAutomated detection of coronary artery disease using different durations of ECG segments with convolutional neural networkKnowl.-Based Syst.2017132627110.1016/j.knosys.2017.06.003 – reference: JhaDKwonG-RContourlet-based feature extraction for computer-aided classification of Alzheimer’s diseaseAlzheimers and Dementia2018147147310.1016/j.jalz.2018.06.2498 – reference: MalikGARobertsonNPTreatments in Alzheimer’s diseaseJ. Neurol.201726424164181:STN:280:DC%2BC1c7ptVSitw%3D%3D10.1007/s00415-017-8395-1281200415306064 – reference: ZhuYHuangCAn improved median filtering algorithm for image noise reductionPhys. Procedia20122560961610.1016/j.phpro.2012.03.133 – reference: N. A. Mathew, R. Vivek, and P. Anurenjan, Early Diagnosis of Alzheimer's Disease from MRI Images Using PNN, in 2018 International CET Conference on Control, Communication, and Computing (IC4), pp. 161–164, 2018. – reference: DickersonBStoubTShahRSperlingRKillianyRAlbertMAlzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adultsNeurology201176139514021:CAS:528:DC%2BC3MXkvValtbw%3D10.1212/WNL.0b013e3182166e96214903233087406 – reference: LimW-QNonseparable Shearlet transformIEEE Trans. Image Process.20132252056206510.1109/TIP.2013.224422323372085 – reference: AyadiWElhamziWCharfiIAtriMA hybrid feature extraction approach for brain MRI classification based on Bag-of-wordsBiomedical Signal Processing and Control20194814415210.1016/j.bspc.2018.10.010 – reference: ChaplotSPatnaikLMJagannathanNRClassification of magnetic resonance brain images using wavelets as input to support vector machine and neural networkBiomedical Signal Processing and Control200611869210.1016/j.bspc.2006.05.002 – reference: ZhangYWangSDetection of Alzheimer’s disease by displacement field and machine learningPeer J20153e125110.7717/peerj.125126401461 – reference: WangSZhangYLiuGPhillipsPYuanT-FDetection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imagingJ. Alzheimers Dis.2016502332481:CAS:528:DC%2BC28XnvVGltA%3D%3D10.3233/JAD-15084826682696 – volume: 305 start-page: 361 year: 2015 ident: 1428_CR35 publication-title: Neuroscience doi: 10.1016/j.neuroscience.2015.08.013 – volume: 94 start-page: 87 year: 2017 ident: 1428_CR46 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2017.05.028 – volume: 22 start-page: 1 issue: 1 year: 2012 ident: 1428_CR12 publication-title: Neuroimaging Clin. N. Am. doi: 10.1016/j.nic.2011.11.002 – volume: 74 start-page: 79 issue: 1-2 year: 2015 ident: 1428_CR48 publication-title: European Neurology doi: 10.1159/000438457 – volume: 44 start-page: 529 issue: 4 year: 2011 ident: 1428_CR45 publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2011.01.001 – volume: 16 start-page: 11 year: 2017 ident: 1428_CR34 publication-title: CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) – volume: 8 start-page: 9161 year: 2018 ident: 1428_CR3 publication-title: Sci. Rep. doi: 10.1038/s41598-018-27337-w – ident: 1428_CR21 doi: 10.1109/CETIC4.2018.8530910 – volume: 50 start-page: 162 year: 2010 ident: 1428_CR28 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.11.046 – volume: 85 start-page: 184 year: 2018 ident: 1428_CR64 publication-title: Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2018.03.023 – volume: 83 start-page: 148 year: 2013 ident: 1428_CR14 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.06.033 – volume: 10 start-page: 371 issue: 4 year: 2011 ident: 1428_CR49 publication-title: Technology in Cancer Research & treatment doi: 10.7785/tcrt.2012.500214 – volume: 264 start-page: 416 issue: 2 year: 2017 ident: 1428_CR5 publication-title: J. Neurol. doi: 10.1007/s00415-017-8395-1 – volume: 420 start-page: 66 year: 2017 ident: 1428_CR62 publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.08.050 – volume: 26 start-page: S1283 year: 2015 ident: 1428_CR38 publication-title: Biomed. Mater. Eng. – volume: 148 start-page: 296 year: 2017 ident: 1428_CR26 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2016.12.026 – volume: 20 start-page: 433 year: 2010 ident: 1428_CR33 publication-title: Digital Signal Processing doi: 10.1016/j.dsp.2009.07.002 – volume: 83 start-page: 109 year: 2017 ident: 1428_CR16 publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2017.02.011 – volume: 14 start-page: 1473 issue: 7 year: 2018 ident: 1428_CR52 publication-title: Alzheimers and Dementia doi: 10.1016/j.jalz.2018.06.2498 – volume: 313 start-page: 1939 issue: 19 year: 2015 ident: 1428_CR10 publication-title: J. Am. Med. Assoc. doi: 10.1001/jama.2015.4669 – volume: 19 start-page: 1166 issue: 5 year: 2010 ident: 1428_CR54 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2010.2041410 – volume: 22 start-page: 123 issue: 6 year: 2005 ident: 1428_CR56 publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2005.1550194 – volume: 1 start-page: 86 issue: 1 year: 2006 ident: 1428_CR59 publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2006.05.002 – volume: 33 start-page: S313 issue: 1 year: 2013 ident: 1428_CR9 publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-2012-129016 – ident: 1428_CR24 doi: 10.1109/SPICES.2015.7091517 – volume: 48 start-page: 144 year: 2019 ident: 1428_CR58 publication-title: Biomedical Signal Processing and Control doi: 10.1016/j.bspc.2018.10.010 – volume: 302 start-page: 14 year: 2018 ident: 1428_CR15 publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2017.12.010 – volume: 12 start-page: 719 issue: 6 year: 2016 ident: 1428_CR20 publication-title: Alzheimer's and Dementia doi: 10.1016/j.jalz.2016.02.010 – volume: 138 start-page: 49 year: 2017 ident: 1428_CR40 publication-title: Comput. Methods Prog. Biomed. doi: 10.1016/j.cmpb.2016.10.007 – volume: 62 start-page: 229 year: 2012 ident: 1428_CR17 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2012.04.056 – volume: 679 start-page: 3 year: 2016 ident: 1428_CR53 publication-title: Communications in Computer and Information Science doi: 10.1007/978-981-10-3274-5_1 – volume: 22 start-page: 2056 issue: 5 year: 2013 ident: 1428_CR55 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2013.2244223 – volume: 183 start-page: 401 year: 2018 ident: 1428_CR19 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2018.08.040 – volume: 50 start-page: 233 year: 2016 ident: 1428_CR36 publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-150848 – volume: 130 start-page: 369 year: 2012 ident: 1428_CR60 publication-title: Prog. Electromagn. Res. doi: 10.2528/PIER12061410 – volume: 78 start-page: 596 issue: 4 year: 2011 ident: 1428_CR7 publication-title: Mt Sinai J. Med. doi: 10.1002/msj.20279 – volume: 1 start-page: 353 issue: 3 year: 2012 ident: 1428_CR2 publication-title: European Journal of Neurodegenerative Diseases – volume: 302 start-page: 66 year: 2018 ident: 1428_CR18 publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2018.01.003 – volume: 77 start-page: 10393 issue: 9 year: 2016 ident: 1428_CR30 publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-016-4222-4 – volume: 197 start-page: 165 issue: 1 year: 2011 ident: 1428_CR27 publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2011.01.027 – volume: 377 start-page: 1465 issue: 9776 year: 2011 ident: 1428_CR6 publication-title: Lancet doi: 10.1016/S0140-6736(11)60582-5 – volume: 182 start-page: 3 issue: 1 year: 2004 ident: 1428_CR11 publication-title: Am. J. Roentgenol. doi: 10.2214/ajr.182.1.1820003 – ident: 1428_CR41 – volume: 6 start-page: 169 issue: 6 year: 2016 ident: 1428_CR57 publication-title: Appl. Sci. doi: 10.3390/app6060169 – volume: 70 start-page: 8 year: 2018 ident: 1428_CR39 publication-title: Comput. Med. Imaging Graph. doi: 10.1016/j.compmedimag.2018.08.002 – volume: 25 start-page: 609 year: 2012 ident: 1428_CR42 publication-title: Phys. Procedia doi: 10.1016/j.phpro.2012.03.133 – volume: 22 start-page: 1250002 issue: 02 year: 2012 ident: 1428_CR43 publication-title: Int. J. Neural Syst. doi: 10.1142/S0129065712500025 – volume: 132 start-page: 62 year: 2017 ident: 1428_CR51 publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2017.06.003 – ident: 1428_CR1 – volume: 90 start-page: 359 year: 2019 ident: 1428_CR50 publication-title: Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2018.08.008 – volume: 65 start-page: 855 year: 2018 ident: 1428_CR29 publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-170069 – volume: 3 start-page: e1251 year: 2015 ident: 1428_CR32 publication-title: Peer J doi: 10.7717/peerj.1251 – volume: 441 start-page: 41 year: 2018 ident: 1428_CR63 publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.01.051 – volume: 5 start-page: 242 issue: 3–4 year: 2007 ident: 1428_CR44 publication-title: Genomics Proteomics Bioinformatics doi: 10.1016/S1672-0229(08)60011-X – ident: 1428_CR47 doi: 10.1007/s12652-018-0854-8 – volume: 9 start-page: 66 year: 2015 ident: 1428_CR37 publication-title: Front. Comput. Neurosci. – volume: 12 start-page: e0177044 issue: 5 year: 2017 ident: 1428_CR4 publication-title: PLOS ONE doi: 10.1371/journal.pone.0177044 – volume: 76 start-page: 1395 year: 2011 ident: 1428_CR23 publication-title: Neurology doi: 10.1212/WNL.0b013e3182166e96 – volume: 5 start-page: 1405 issue: 4 year: 2010 ident: 1428_CR13 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2010.03.051 – volume: 144 start-page: 171 year: 2014 ident: 1428_CR31 publication-title: Prog. Electromagn. Res. doi: 10.2528/PIER13121310 – volume: 6 start-page: 51 year: 2014 ident: 1428_CR8 publication-title: Alzheimers Res. Ther. doi: 10.1186/alzrt276 – volume: 20 start-page: 70 year: 2017 ident: 1428_CR61 publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2017.02.006 – volume: 21 start-page: 681 issue: 1 year: 2017 ident: 1428_CR22 publication-title: Clust. Comput. doi: 10.1007/s10586-017-0977-2 – volume: 46 start-page: 142 year: 2016 ident: 1428_CR25 publication-title: International Journal of Current Engineering and Technology
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Snippet	The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The... The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The...
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SubjectTerms	Abnormalities Alzheimer Disease - classification Alzheimer Disease - diagnostic imaging Alzheimer's disease Brain Brain - pathology Classification Cognitive ability Comparative analysis Diagnosis Diagnosis, Computer-Assisted - methods Feature extraction Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image acquisition Image detection Image Interpretation, Computer-Assisted - methods Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Medicine Medicine & Public Health Neuroimaging NMR Nuclear magnetic resonance Pattern Recognition, Automated - methods Recent Advances in Deep Learning for Biomedical Signal Processing Sensitivity Series (mathematics) Statistics for Life Sciences
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Title	Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques
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