A Hybrid Deep Contractive Autoencoder and Restricted Boltzmann Machine Approach to Differentiate Representation of Female Brain Disorder
Deep Learning approach dragged the full attention of researcher in medical images due to their superior literature reported success and promising directions. Concluding the most discriminative set of features greatly represents a valuable guide for achieving the satisfaction performance of a medical...
Uložené v:
| Vydané v: | Procedia computer science Ročník 176; s. 1033 - 1042 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
2020
|
| Predmet: | |
| ISSN: | 1877-0509, 1877-0509 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Deep Learning approach dragged the full attention of researcher in medical images due to their superior literature reported success and promising directions. Concluding the most discriminative set of features greatly represents a valuable guide for achieving the satisfaction performance of a medical diagnosing system. Despite, many methods proposed for such objective, the restricted Boltzmann machines outperform as they learn features directly from data, however they lack the optimal classification performance due to data complexity. Additionally, the contractive au-toencoder offers regularized term that explicitly increases the robustness of features representation and enhancement in overall performance. This paper proposes a novel deep learning framework for diagnosing female brain disorder from fMRI scans. The configuration combines the contractive autoencoder and the discriminative restricted Boltz-mann machine (DRBM) as we seek an improvement for the classification of fMRI. The demonstrated effectiveness of the contractive autoencoder supports fitting the probability distribution model of the DRBM and transfer learning to a deeper level. Our experimental indicates that the proposed model is able to detect female brain disorder with an accuracy of 88.17% and improved literature reported results on common issues. |
|---|---|
| AbstractList | Deep Learning approach dragged the full attention of researcher in medical images due to their superior literature reported success and promising directions. Concluding the most discriminative set of features greatly represents a valuable guide for achieving the satisfaction performance of a medical diagnosing system. Despite, many methods proposed for such objective, the restricted Boltzmann machines outperform as they learn features directly from data, however they lack the optimal classification performance due to data complexity. Additionally, the contractive au-toencoder offers regularized term that explicitly increases the robustness of features representation and enhancement in overall performance. This paper proposes a novel deep learning framework for diagnosing female brain disorder from fMRI scans. The configuration combines the contractive autoencoder and the discriminative restricted Boltz-mann machine (DRBM) as we seek an improvement for the classification of fMRI. The demonstrated effectiveness of the contractive autoencoder supports fitting the probability distribution model of the DRBM and transfer learning to a deeper level. Our experimental indicates that the proposed model is able to detect female brain disorder with an accuracy of 88.17% and improved literature reported results on common issues. |
| Author | Alrowais, Fadwa Karamti, Hanen M.Mahmoud, Abeer |
| Author_xml | – sequence: 1 givenname: Abeer surname: M.Mahmoud fullname: M.Mahmoud, Abeer email: abeer_f13@yahoo.com organization: Computer Sciences Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt – sequence: 2 givenname: Fadwa surname: Alrowais fullname: Alrowais, Fadwa organization: Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia – sequence: 3 givenname: Hanen surname: Karamti fullname: Karamti, Hanen organization: Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia |
| BookMark | eNqFkMtKQzEQhoNUsNY-gZu8QGty7lm46MVaQRFE1yEnmWDKaXJIYqE-gY9tal2ICx0G5g_MN5n5z9HAOgsIXVIypYRWV5tp750M04xkZEpYSnaChrSp6wkpCRv80GdoHMKGpMibhtF6iD5meL1vvVF4CdDjhbPRCxnNDvDsLTqw0inwWFiFnyBEb2QEheeui-9bYS1-EPLV2NTcpyWSxtHhpdEaPNhoRISE9R5CeolonMVO4xVsRQd47oWxqTk4n764QKdadAHG33WEXlY3z4v15P7x9m4xu5_IrMrZpCiVaEoNTNGGyLLWFWsLyapcNzrpgjSs1RXVNRFNIVpVVRlNSKszIkDqPB8hdpwrvQvBg-bSHFdLh5uOU8IPrvIN_3KVH1zlhKVkic1_sb03W-H3_1DXRwrSWTsDngdpkrGgjAcZuXLmT_4T8FOXxQ |
| CitedBy_id | crossref_primary_10_1007_s13369_021_05674_9 |
| Cites_doi | 10.1109/CDC.2012.6427042 10.1038/mp.2013.78 10.1016/j.media.2016.08.003 10.1109/ISBI.2018.8363798 10.1016/j.jaac.2012.05.018 10.3389/fnins.2017.00460 10.5220/0009397605400547 10.1007/s40489-019-00158-x 10.1016/j.procs.2018.07.213 10.1109/TMI.2016.2553401 10.1007/s10803-008-0558-6 10.3389/fnhum.2013.00599 10.1007/978-3-030-00919-9_35 10.1007/s10278-018-0093-8 10.1109/BIBM.2018.8621472 10.1016/j.nicl.2017.08.017 10.1016/j.nicl.2014.12.013 10.1016/j.procs.2019.09.168 10.1017/S0954579408000370 10.1016/j.neuroimage.2014.03.048 10.1016/j.procs.2019.09.265 10.1007/978-3-030-00931-1_24 10.1016/j.neuroimage.2016.10.001 10.1038/ncomms15037 10.1137/130924688 10.1007/s11682-018-9899-8 10.1016/j.cortex.2014.08.011 10.1016/j.pnpbp.2015.06.014 |
| ContentType | Journal Article |
| Copyright | 2020 |
| Copyright_xml | – notice: 2020 |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.procs.2020.09.099 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1877-0509 |
| EndPage | 1042 |
| ExternalDocumentID | 10_1016_j_procs_2020_09_099 S1877050920319992 |
| GroupedDBID | --K 0R~ 0SF 1B1 457 5VS 6I. 71M AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAQFI AAXUO ABMAC ACGFS ADBBV ADEZE AEXQZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ E3Z EBS EJD EP3 FDB FNPLU HZ~ IXB KQ8 M41 M~E NCXOZ O-L O9- OK1 P2P RIG ROL SES SSZ 9DU AAYWO AAYXX ABWVN ACRPL ACVFH ADCNI ADNMO ADVLN AEUPX AFPUW AIGII AKBMS AKRWK AKYEP CITATION ~HD |
| ID | FETCH-LOGICAL-c2639-45da85fe9d180c57f69b4c963f8ff694089bf61f70a84abd66215dabf20aecf33 |
| ISSN | 1877-0509 |
| IngestDate | Tue Nov 18 22:11:18 EST 2025 Sat Nov 29 06:55:49 EST 2025 Wed May 17 01:22:45 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Restricted Boltzmann Machine Deep Learning Contractive Autoencoder |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c2639-45da85fe9d180c57f69b4c963f8ff694089bf61f70a84abd66215dabf20aecf33 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.procs.2020.09.099 |
| PageCount | 10 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_procs_2020_09_099 crossref_primary_10_1016_j_procs_2020_09_099 elsevier_sciencedirect_doi_10_1016_j_procs_2020_09_099 |
| PublicationCentury | 2000 |
| PublicationDate | 2020 2020-00-00 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – year: 2020 text: 2020 |
| PublicationDecade | 2020 |
| PublicationTitle | Procedia computer science |
| PublicationYear | 2020 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Xi. Li, N. Dvornek, J. Zhuang, P. Ventolaz, and J. Duncan, (2018), "Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI". Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, MICCAI: Medical Image Computing and Computer Assisted Intervention, pp.206--214. Xi. Li, N. Dvornek, J. Zhuang, L. Staib, P. Ventola, and J. Duncan, (2018), "2-Channel Convolutional 3d Deep Neural Network (2CC3D) For fMRI Analysis: ASD Classification and Feature Learning". IEEE 15th Int. Symposium on Biomedical Imaging (ISBI). Rossi (bib4) 2019; vol.159 Hinton (bib16) 2012 Iidaka (bib22) 2015; vol.63 Heinsfeld, Franco, Buchweitz, Meneguzzi (bib18) 2018; 17 Dworzynski, Ronald, Bolton, Happe (bib10) 2012; vol.51 Hinton (bib19) 2010; 9 Hyde (bib21) 2019; 6 Ximenes, Fambrini, Goulart, Saito (bib24) 2019; vol.159 Friedman, Hastie, Tibshirani (bib14) 2001; volume 1 Martino (bib9) 2014; vol.19 Xing, X., Ji, J., and Yao, Y. (2018). "Convolutional neural network with element wise filters to extract hierarchical topo-logical features for brain networks," in IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), pp.780--783. Williams (bib31) 2008; vol.38 Li, Liu, Sun, Shen, Wang (bib23) 2018; vol.11046 Choi, H. (2017). "Functional connectivity patterns of autism spectrum disorder identified by deep feature Learning". Dawson (bib11) 2008; vol.20 Greenspan, Summers, van Ginneken (bib17) 2016; 35 Plitt, Barnes, Martin (bib26) 2015; vol.7 Horikawa, Kamitani (bib20) 2017; 8 Guo, Dominick, Minai Li, Erickson, Lu (bib32) 2017; 11 Subbaraju, Suresh, Sundaram, Narasimhan (bib30) 2017; vol.35 Kim, Tagare (bib36) 2014; vol.7 Eickenberg, Gramfort, Varoquaux, Thirion (bib12) 2017; vol.152 Aghdam, Sharifi, Pedram (bib3) 2018; 31 S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio,(2011)," Contractive auto-encoders: Explicit invariance during feature extraction", in Proc. 28th Int.Conf.Machine Learning (ICML-11), pp.833--840. Fenske, Zalenski, Krantz, McClannahan (bib13) 1985; vol.5 Zu, Gao, Munsell (bib6) 2018; vol.13 Alain, Bengio (bib15) 2014; 15 Hjelm (bib27) 2014; vol.96 Chen (bib7) 2016; vol.64 Schirru, A., Susto, G.A., Pampuri, S., McLoone, S., (2012). "Learning from time series: Supervised aggregative feature extraction", in: Decision and Control (CDC), IEEE 51st Annual Conf., pp.5254--5259. Abeer M. Mahmoud, Hanen Karamti and Fadwa Alrowais,(2020) "An Effective Sparse Autoencoders Based Deep Learning Framework for fMRI Scans Classification", proceeding of 22nd international Conference of enterprise information systems (ICEIS) vol.1, pp.540--547. Nielsen (bib25) 2013; vol.7 Hervella, Rouco, Novo, Ortega (bib5) 2018; vol.126 Abeer Mahmoud, Karamti, Alrowais, Hadjouni (bib1) 2020; 29 10.1016/j.procs.2020.09.099_bib2 Hjelm (10.1016/j.procs.2020.09.099_bib27) 2014; vol.96 Eickenberg (10.1016/j.procs.2020.09.099_bib12) 2017; vol.152 Plitt (10.1016/j.procs.2020.09.099_bib26) 2015; vol.7 Friedman (10.1016/j.procs.2020.09.099_bib14) 2001; volume 1 Aghdam (10.1016/j.procs.2020.09.099_bib3) 2018; 31 Nielsen (10.1016/j.procs.2020.09.099_bib25) 2013; vol.7 Guo (10.1016/j.procs.2020.09.099_bib32) 2017; 11 10.1016/j.procs.2020.09.099_bib35 Hinton (10.1016/j.procs.2020.09.099_bib16) 2012 10.1016/j.procs.2020.09.099_bib33 10.1016/j.procs.2020.09.099_bib34 Williams (10.1016/j.procs.2020.09.099_bib31) 2008; vol.38 Ximenes (10.1016/j.procs.2020.09.099_bib24) 2019; vol.159 Rossi (10.1016/j.procs.2020.09.099_bib4) 2019; vol.159 Hyde (10.1016/j.procs.2020.09.099_bib21) 2019; 6 Alain (10.1016/j.procs.2020.09.099_bib15) 2014; 15 Martino (10.1016/j.procs.2020.09.099_bib9) 2014; vol.19 Horikawa (10.1016/j.procs.2020.09.099_bib20) 2017; 8 10.1016/j.procs.2020.09.099_bib8 Dworzynski (10.1016/j.procs.2020.09.099_bib10) 2012; vol.51 Hervella (10.1016/j.procs.2020.09.099_bib5) 2018; vol.126 Zu (10.1016/j.procs.2020.09.099_bib6) 2018; vol.13 Chen (10.1016/j.procs.2020.09.099_bib7) 2016; vol.64 Kim (10.1016/j.procs.2020.09.099_bib36) 2014; vol.7 Li (10.1016/j.procs.2020.09.099_bib23) 2018; vol.11046 Hinton (10.1016/j.procs.2020.09.099_bib19) 2010; 9 Subbaraju (10.1016/j.procs.2020.09.099_bib30) 2017; vol.35 Iidaka (10.1016/j.procs.2020.09.099_bib22) 2015; vol.63 10.1016/j.procs.2020.09.099_bib28 10.1016/j.procs.2020.09.099_bib29 Abeer Mahmoud (10.1016/j.procs.2020.09.099_bib1) 2020; 29 Fenske (10.1016/j.procs.2020.09.099_bib13) 1985; vol.5 Greenspan (10.1016/j.procs.2020.09.099_bib17) 2016; 35 Heinsfeld (10.1016/j.procs.2020.09.099_bib18) 2018; 17 Dawson (10.1016/j.procs.2020.09.099_bib11) 2008; vol.20 |
| References_xml | – volume: vol.159 start-page: 135 year: 2019 end-page: 144 ident: bib24 article-title: “Coffee Leaf Disease Recognition Based on Deep Learning and Texture Attributes” publication-title: Procedia Computer Science – volume: vol.7 start-page: 359 year: 2015 end-page: 366 ident: bib26 article-title: “Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards” publication-title: Neuroimage Clin. – volume: volume 1 year: 2001 ident: bib14 article-title: “The elements of statistical learning” publication-title: Springer series in statistics. – volume: vol.63 start-page: 55 year: 2015 end-page: 67 ident: bib22 article-title: “Resting state functional magnetic resonance imaging and neural network classified autism and control” publication-title: Cortex – volume: vol.7 start-page: 528 year: 2014 end-page: 557 ident: bib36 article-title: “Intensity Nonuniformity Correction for Brain MR Images with Known Voxel Classes” publication-title: SiAM 1. imaging Sci. – volume: vol.64 start-page: 1 year: 2016 end-page: 9 ident: bib7 article-title: “Multivariate classification of autism spectrum disorder using frequency specific resting-state functional connectivity a multi-center study” publication-title: Prog. Neuro-psychopharmacol. Biol. Psychiatry. – reference: S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio,(2011)," Contractive auto-encoders: Explicit invariance during feature extraction", in Proc. 28th Int.Conf.Machine Learning (ICML-11), pp.833--840. – volume: vol.38 start-page: 1731 year: 2008 end-page: 1739 ident: bib31 article-title: “ The childhood autism spectrum test (cast): sex differences” publication-title: J. Autism Dev. Disord. – volume: vol.7 start-page: 599 year: 2013 ident: bib25 article-title: “ Multisite functional connectivity MRI classification of autism: ABIDE results” publication-title: Front. Hum. Neurosci. – volume: vol.19 start-page: 659 year: 2014 end-page: 667 ident: bib9 article-title: “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism” publication-title: Mol. Psychiatry – volume: 15 start-page: 3563 year: 2014 end-page: 3593 ident: bib15 article-title: “ What regularized auto-encoders learn from the data-generating distribution” publication-title: J. Mach. Learn. Res. – volume: 29 start-page: 3111 year: 2020 end-page: 3123 ident: bib1 article-title: “Effective Health Care Analytics & Diagnosing using Artificial intelligence Approaches” publication-title: International Journal of Advanced Science and Technology – volume: 8 start-page: 15037 year: 2017 ident: bib20 article-title: “Generic decoding of seen and imagined objects using hierarchical visual features” publication-title: Nat Commun – reference: Schirru, A., Susto, G.A., Pampuri, S., McLoone, S., (2012). "Learning from time series: Supervised aggregative feature extraction", in: Decision and Control (CDC), IEEE 51st Annual Conf., pp.5254--5259. – volume: vol.152 start-page: 184 year: 2017 end-page: 194 ident: bib12 article-title: “ Seeing it all: convolutional network layers map the function of the human visual system” publication-title: NeuroImage – reference: Xi. Li, N. Dvornek, J. Zhuang, L. Staib, P. Ventola, and J. Duncan, (2018), "2-Channel Convolutional 3d Deep Neural Network (2CC3D) For fMRI Analysis: ASD Classification and Feature Learning". IEEE 15th Int. Symposium on Biomedical Imaging (ISBI). – start-page: 599 year: 2012 end-page: 619 ident: bib16 article-title: “A practical guide to training restricted Boltzmann machines” publication-title: in Neural Networks – volume: 6 start-page: 1 year: 2019 end-page: 19 ident: bib21 article-title: “ Applications of supervised machine learning in autism spectrum disorder research: a review” publication-title: Rev. J. Autism Develop. Disord – volume: 35 start-page: 1153 year: 2016 end-page: 1159 ident: bib17 article-title: “Deep learning in medical imaging: Overview and future promise of an exciting new technique” publication-title: IEEE Trans Med Imaging – reference: Choi, H. (2017). "Functional connectivity patterns of autism spectrum disorder identified by deep feature Learning". – volume: vol.159 start-page: 981 year: 2019 end-page: 989 ident: bib4 article-title: “Analysis of brain NMR images for age estimation with deep learning” publication-title: Procedia Computer Science – volume: 9 start-page: 926 year: 2010 ident: bib19 article-title: “A practical guide to training restricted Boltzmann machines” publication-title: Momentum – reference: Xing, X., Ji, J., and Yao, Y. (2018). "Convolutional neural network with element wise filters to extract hierarchical topo-logical features for brain networks," in IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), pp.780--783. – volume: 17 start-page: 16 year: 2018 end-page: 23 ident: bib18 article-title: “Identification of autism spectrum disorder using deep learning and the ABIDE dataset” publication-title: Neuroimage Clin. – volume: vol.35 start-page: 375 year: 2017 end-page: 389 ident: bib30 article-title: “Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging: a spatial filtering approach” publication-title: Medical Image Analysis. – volume: 11 year: 2017 ident: bib32 article-title: “Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method” publication-title: Front. Neurosci. – volume: vol.96 start-page: 245 year: 2014 end-page: 260 ident: bib27 article-title: “Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks” publication-title: NeuroImage – volume: vol.5 start-page: 49 year: 1985 end-page: 58 ident: bib13 article-title: “Age at intervention and treatment outcome for autistic children in a comprehensive intervention program” publication-title: Anal. Interv. Dev. Disabil. – volume: vol.13 start-page: 879 year: 2018 end-page: 892 ident: bib6 article-title: “ Identifying disease-related sub-network connectome biomarkers by sparse hyper-graph learning” publication-title: Brain Imaging and Behavior – volume: vol.11046 start-page: 303 year: 2018 end-page: 309 ident: bib23 article-title: “Early diagnosis of autism disease by multi-channel CNNs” publication-title: Mach. Learn. Med. Imaging – volume: vol.126 start-page: 97 year: 2018 end-page: 104 ident: bib5 article-title: “Multimodal registration of retinal images using domain-specific landmarks and vessel enhancement” publication-title: Procedia Computer Science – volume: vol.51 start-page: 788 year: 2012 end-page: 797 ident: bib10 article-title: “ How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders” publication-title: J.Am. Acad. Child Adolesc. Psychiatry – volume: vol.20 start-page: 775 year: 2008 ident: bib11 article-title: “Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder” publication-title: Dev. Psychopathol. – reference: Xi. Li, N. Dvornek, J. Zhuang, P. Ventolaz, and J. Duncan, (2018), "Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI". Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, MICCAI: Medical Image Computing and Computer Assisted Intervention, pp.206--214. – reference: Abeer M. Mahmoud, Hanen Karamti and Fadwa Alrowais,(2020) "An Effective Sparse Autoencoders Based Deep Learning Framework for fMRI Scans Classification", proceeding of 22nd international Conference of enterprise information systems (ICEIS) vol.1, pp.540--547. – volume: 31 start-page: 895 year: 2018 end-page: 903 ident: bib3 article-title: “Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network” publication-title: J. Digit. Imaging – volume: 29 start-page: 3111 issue: no. 5 year: 2020 ident: 10.1016/j.procs.2020.09.099_bib1 article-title: “Effective Health Care Analytics & Diagnosing using Artificial intelligence Approaches” publication-title: International Journal of Advanced Science and Technology – volume: 15 start-page: 3563 issue: no. 1 year: 2014 ident: 10.1016/j.procs.2020.09.099_bib15 article-title: “ What regularized auto-encoders learn from the data-generating distribution” publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.procs.2020.09.099_bib29 doi: 10.1109/CDC.2012.6427042 – volume: vol.19 start-page: 659 year: 2014 ident: 10.1016/j.procs.2020.09.099_bib9 article-title: “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism” publication-title: Mol. Psychiatry doi: 10.1038/mp.2013.78 – volume: vol.35 start-page: 375 year: 2017 ident: 10.1016/j.procs.2020.09.099_bib30 article-title: “Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging: a spatial filtering approach” publication-title: Medical Image Analysis. doi: 10.1016/j.media.2016.08.003 – ident: 10.1016/j.procs.2020.09.099_bib34 doi: 10.1109/ISBI.2018.8363798 – volume: vol.51 start-page: 788 year: 2012 ident: 10.1016/j.procs.2020.09.099_bib10 article-title: “ How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders” publication-title: J.Am. Acad. Child Adolesc. Psychiatry doi: 10.1016/j.jaac.2012.05.018 – volume: 11 year: 2017 ident: 10.1016/j.procs.2020.09.099_bib32 article-title: “Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method” publication-title: Front. Neurosci. doi: 10.3389/fnins.2017.00460 – volume: vol.5 start-page: 49 year: 1985 ident: 10.1016/j.procs.2020.09.099_bib13 article-title: “Age at intervention and treatment outcome for autistic children in a comprehensive intervention program” publication-title: Anal. Interv. Dev. Disabil. – ident: 10.1016/j.procs.2020.09.099_bib28 – ident: 10.1016/j.procs.2020.09.099_bib2 doi: 10.5220/0009397605400547 – volume: 6 start-page: 1 year: 2019 ident: 10.1016/j.procs.2020.09.099_bib21 article-title: “ Applications of supervised machine learning in autism spectrum disorder research: a review” publication-title: Rev. J. Autism Develop. Disord doi: 10.1007/s40489-019-00158-x – volume: vol.126 start-page: 97 year: 2018 ident: 10.1016/j.procs.2020.09.099_bib5 article-title: “Multimodal registration of retinal images using domain-specific landmarks and vessel enhancement” publication-title: Procedia Computer Science doi: 10.1016/j.procs.2018.07.213 – volume: 35 start-page: 1153 issue: 5 year: 2016 ident: 10.1016/j.procs.2020.09.099_bib17 article-title: “Deep learning in medical imaging: Overview and future promise of an exciting new technique” publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2016.2553401 – volume: vol.38 start-page: 1731 year: 2008 ident: 10.1016/j.procs.2020.09.099_bib31 article-title: “ The childhood autism spectrum test (cast): sex differences” publication-title: J. Autism Dev. Disord. doi: 10.1007/s10803-008-0558-6 – volume: vol.7 start-page: 599 year: 2013 ident: 10.1016/j.procs.2020.09.099_bib25 article-title: “ Multisite functional connectivity MRI classification of autism: ABIDE results” publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2013.00599 – volume: 9 start-page: 926 issue: 1 year: 2010 ident: 10.1016/j.procs.2020.09.099_bib19 article-title: “A practical guide to training restricted Boltzmann machines” publication-title: Momentum – volume: vol.11046 start-page: 303 year: 2018 ident: 10.1016/j.procs.2020.09.099_bib23 article-title: “Early diagnosis of autism disease by multi-channel CNNs” publication-title: Mach. Learn. Med. Imaging doi: 10.1007/978-3-030-00919-9_35 – volume: 31 start-page: 895 year: 2018 ident: 10.1016/j.procs.2020.09.099_bib3 article-title: “Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network” publication-title: J. Digit. Imaging doi: 10.1007/s10278-018-0093-8 – ident: 10.1016/j.procs.2020.09.099_bib35 doi: 10.1109/BIBM.2018.8621472 – volume: 17 start-page: 16 year: 2018 ident: 10.1016/j.procs.2020.09.099_bib18 article-title: “Identification of autism spectrum disorder using deep learning and the ABIDE dataset” publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2017.08.017 – volume: vol.7 start-page: 359 year: 2015 ident: 10.1016/j.procs.2020.09.099_bib26 article-title: “Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards” publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2014.12.013 – volume: vol.159 start-page: 135 year: 2019 ident: 10.1016/j.procs.2020.09.099_bib24 article-title: “Coffee Leaf Disease Recognition Based on Deep Learning and Texture Attributes” publication-title: Procedia Computer Science doi: 10.1016/j.procs.2019.09.168 – volume: volume 1 year: 2001 ident: 10.1016/j.procs.2020.09.099_bib14 article-title: “The elements of statistical learning” publication-title: Springer series in statistics. – volume: vol.20 start-page: 775 year: 2008 ident: 10.1016/j.procs.2020.09.099_bib11 article-title: “Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder” publication-title: Dev. Psychopathol. doi: 10.1017/S0954579408000370 – ident: 10.1016/j.procs.2020.09.099_bib8 – volume: vol.96 start-page: 245 year: 2014 ident: 10.1016/j.procs.2020.09.099_bib27 article-title: “Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks” publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.03.048 – volume: vol.159 start-page: 981 year: 2019 ident: 10.1016/j.procs.2020.09.099_bib4 article-title: “Analysis of brain NMR images for age estimation with deep learning” publication-title: Procedia Computer Science doi: 10.1016/j.procs.2019.09.265 – start-page: 599 year: 2012 ident: 10.1016/j.procs.2020.09.099_bib16 article-title: “A practical guide to training restricted Boltzmann machines” publication-title: in Neural Networks – ident: 10.1016/j.procs.2020.09.099_bib33 doi: 10.1007/978-3-030-00931-1_24 – volume: vol.152 start-page: 184 year: 2017 ident: 10.1016/j.procs.2020.09.099_bib12 article-title: “ Seeing it all: convolutional network layers map the function of the human visual system” publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.10.001 – volume: 8 start-page: 15037 year: 2017 ident: 10.1016/j.procs.2020.09.099_bib20 article-title: “Generic decoding of seen and imagined objects using hierarchical visual features” publication-title: Nat Commun doi: 10.1038/ncomms15037 – volume: vol.7 start-page: 528 issue: no.1 year: 2014 ident: 10.1016/j.procs.2020.09.099_bib36 article-title: “Intensity Nonuniformity Correction for Brain MR Images with Known Voxel Classes” publication-title: SiAM 1. imaging Sci. doi: 10.1137/130924688 – volume: vol.13 start-page: 879 issue: 4 year: 2018 ident: 10.1016/j.procs.2020.09.099_bib6 article-title: “ Identifying disease-related sub-network connectome biomarkers by sparse hyper-graph learning” publication-title: Brain Imaging and Behavior doi: 10.1007/s11682-018-9899-8 – volume: vol.63 start-page: 55 year: 2015 ident: 10.1016/j.procs.2020.09.099_bib22 article-title: “Resting state functional magnetic resonance imaging and neural network classified autism and control” publication-title: Cortex doi: 10.1016/j.cortex.2014.08.011 – volume: vol.64 start-page: 1 year: 2016 ident: 10.1016/j.procs.2020.09.099_bib7 article-title: “Multivariate classification of autism spectrum disorder using frequency specific resting-state functional connectivity a multi-center study” publication-title: Prog. Neuro-psychopharmacol. Biol. Psychiatry. doi: 10.1016/j.pnpbp.2015.06.014 |
| SSID | ssj0000388917 |
| Score | 2.211495 |
| Snippet | Deep Learning approach dragged the full attention of researcher in medical images due to their superior literature reported success and promising directions.... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 1033 |
| SubjectTerms | Contractive Autoencoder Deep Learning Restricted Boltzmann Machine |
| Title | A Hybrid Deep Contractive Autoencoder and Restricted Boltzmann Machine Approach to Differentiate Representation of Female Brain Disorder |
| URI | https://dx.doi.org/10.1016/j.procs.2020.09.099 |
| Volume | 176 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1877-0509 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000388917 issn: 1877-0509 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Li9swEBZh20MvfZduty069Ja6OIpjSUf3EQKlS6Fb2JuRbYndJbFN6mS3e-i5f6T_szOS_Fg2LG2hEIwjIitkvsyMxHzfEPJqqqZGZGoSMG5gg6LZNBCxFMEMcl2pdBZpZWyzCX54KI6P5efR6FfLhdkueVmKiwtZ_1dTwxgYG6mzf2Hu7qEwAPdgdLiC2eH6R4ZPxovvSMMCV6JrZPQ1lgi11eNk01SoW4nyEY6XiE07csw531bL5nKlyhI7EZ1g5pnUPdnqve-iAt6gwT4Pdc9ZstnmXK8gzgBO1GnZ6XkO015LRwAk2gp2bCIx9pG3s_ebT-pkVW3cKXem-5rhZLmuzpVTQpir4rwLIx_VWq1cMcJClZ7R5s8vWNgfp12j1FgPLDgPUJTGBagdY63b5kPHOwmdnoYP4rDJZDsDhDurOMPwlKNaOwutzK2UfTzsqhS_4Lq4LEOql5QQ6W8xDjswrBD90R_loaCOtL2duy_a6lvZSsJra-3OgQZ5zdF9ctdvSGjigPSAjHT5kNxrm31Q7_sfkZ8JdbiiiCs6wBUd4IoCrmiPK9rhinpc0RZXtKnoFVzRq7iilaEOV9Tiira4eky-zj8cvVsEvo1HkLMY6wxmhRJY01hMRJjPuIllFuXg-I0wcB-FQmYmnhgeKhGprIhjSEMLlRkWKp2b6fQJ2SurUj8lVBhwIRnLcBMTxZqrQmRYxjDjMA9stU9Y-8Omude4x1Yry7QtZjxLrTVStEYaSnjJffK6m1Q7iZebPx63Fkv9f8Vlnylg7KaJz_514gG5g-_cwd9zstesN_oFuZ1vm9Nv65cWi78BTLi8-A |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Hybrid+Deep+Contractive+Autoencoder+and+Restricted+Boltzmann+Machine+Approach+to+Differentiate+Representation+of+Female+Brain+Disorder&rft.jtitle=Procedia+computer+science&rft.au=M.Mahmoud%2C+Abeer&rft.au=Alrowais%2C+Fadwa&rft.au=Karamti%2C+Hanen&rft.date=2020&rft.pub=Elsevier+B.V&rft.issn=1877-0509&rft.eissn=1877-0509&rft.volume=176&rft.spage=1033&rft.epage=1042&rft_id=info:doi/10.1016%2Fj.procs.2020.09.099&rft.externalDocID=S1877050920319992 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-0509&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-0509&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-0509&client=summon |