Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer's Disease
The development of computerized healthcare has been powered by diagnostic imaging and machine learning techniques. In particular, recent advances in deep learning have opened a new era in support of multimedia healthcare distribution. For earlier detection of Alzheimer's disease, the study sugg...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.115383-115392 |
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| creator | Guo, Haibing Zhang, Yongjin |
| description | The development of computerized healthcare has been powered by diagnostic imaging and machine learning techniques. In particular, recent advances in deep learning have opened a new era in support of multimedia healthcare distribution. For earlier detection of Alzheimer's disease, the study suggested the Improved Deep Learning Algorithm (IDLA) and statistically significant text information. The specific information in clinical text includes the age, sex and genes of the person and apolipoprotein E; the brain function is established using resting-state functional data (MRI) for the measurement of connectivity in the brain regions. A specialized network of autoencoders is used in earlier diagnosis to distinguish between natural aging and disorder progression. The suggested approach incorporates effectively biased neural network functionality and allows a reliable Alzheimer's disease recognition. In comparison with conventional classifiers depends on time series R-fMRI results, the proposed deep learning algorithm has improved significantly and, in the best cases, the standard deviation reduced by 45%, indicating the forecast model is more reliable and efficient in relation to conventional methodologies. The work examines the benefits of improved deep learning algorithms from recognizing high-dimensional information in healthcare and can lead to the early diagnosis and prevention of Alzheimer's disease. |
| doi_str_mv | 10.1109/ACCESS.2020.3003424 |
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In comparison with conventional classifiers depends on time series R-fMRI results, the proposed deep learning algorithm has improved significantly and, in the best cases, the standard deviation reduced by 45%, indicating the forecast model is more reliable and efficient in relation to conventional methodologies. 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(IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>44</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000549156000001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c408t-f907104bd75f5f0d953a2faa0456289971f618f0cf12a190f57d5ebc41c73fc63</citedby><cites>FETCH-LOGICAL-c408t-f907104bd75f5f0d953a2faa0456289971f618f0cf12a190f57d5ebc41c73fc63</cites><orcidid>0000-0002-7182-6016 ; 0000-0002-7159-7778</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9119997$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,865,2103,2115,4025,27638,27928,27929,27930,28253,54938</link.rule.ids></links><search><creatorcontrib>Guo, Haibing</creatorcontrib><creatorcontrib>Zhang, Yongjin</creatorcontrib><title>Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer's Disease</title><title>IEEE access</title><addtitle>Access</addtitle><addtitle>IEEE ACCESS</addtitle><description>The development of computerized healthcare has been powered by diagnostic imaging and machine learning techniques. In particular, recent advances in deep learning have opened a new era in support of multimedia healthcare distribution. For earlier detection of Alzheimer's disease, the study suggested the Improved Deep Learning Algorithm (IDLA) and statistically significant text information. The specific information in clinical text includes the age, sex and genes of the person and apolipoprotein E; the brain function is established using resting-state functional data (MRI) for the measurement of connectivity in the brain regions. A specialized network of autoencoders is used in earlier diagnosis to distinguish between natural aging and disorder progression. The suggested approach incorporates effectively biased neural network functionality and allows a reliable Alzheimer's disease recognition. In comparison with conventional classifiers depends on time series R-fMRI results, the proposed deep learning algorithm has improved significantly and, in the best cases, the standard deviation reduced by 45%, indicating the forecast model is more reliable and efficient in relation to conventional methodologies. The work examines the benefits of improved deep learning algorithms from recognizing high-dimensional information in healthcare and can lead to the early diagnosis and prevention of Alzheimer's disease.</description><subject>Aging (natural)</subject><subject>Algorithms</subject><subject>Alzheimer's disease</subject><subject>autoencoder network</subject><subject>Brain</subject><subject>Brain modeling</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Data models</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Functional magnetic resonance imaging</subject><subject>Health care</subject><subject>improved deep learning algorithm (IDLA)</subject><subject>Machine learning</subject><subject>Multimedia</subject><subject>Neural networks</subject><subject>R-fMRI data</subject><subject>Science & Technology</subject><subject>Technology</subject><subject>Telecommunications</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkU1rGzEQhpfSQkKaX5CLoIceil3N6mt1NBunNbgU4uYstNqRI2OvXGndkv76yt2Q9liBkBiedyTmqaoboHMAqj8u2na52cxrWtM5o5Txmr-qLmuQesYEk6__uV9U1znvaFlNKQl1WfX3mMcwbMlmtCMS_-V-RezQk9XhmOIP7Mkt4pGs0abhTC3225jC-HggPiaytGkfMBVmRDeGOJDoC_LrEcMB0_tMbkNGm_Ft9cbbfcbr5_Oqerhbfms_z9ZfP63axXrmOG3GmddUAeVdr4QXnvZaMFt7aykXsm60VuAlNJ46D7UFTb1QvcDOcXCKeSfZVbWa-vbR7swxhYNNTybaYP4UYtoam8bg9miUkB0D1okekSOqTqjGOgESPHrtXen1bupV5vD9VIZkdvGUhvJ9U3PBJZQtCsUmyqWYc0L_8ipQc7ZjJjvmbMc82ympD1PqJ3bRZxdwcPiSLHYE1yDk2ROFQjf_T7eheCwm2ngaxhK9maIB8W9EA-gyTfYb1iqrCg</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Guo, Haibing</creator><creator>Zhang, Yongjin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AOWDO</scope><scope>BLEPL</scope><scope>DTL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-7182-6016</orcidid><orcidid>https://orcid.org/0000-0002-7159-7778</orcidid></search><sort><creationdate>2020</creationdate><title>Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer's Disease</title><author>Guo, Haibing ; Zhang, Yongjin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-f907104bd75f5f0d953a2faa0456289971f618f0cf12a190f57d5ebc41c73fc63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aging (natural)</topic><topic>Algorithms</topic><topic>Alzheimer's disease</topic><topic>autoencoder network</topic><topic>Brain</topic><topic>Brain modeling</topic><topic>Computer Science</topic><topic>Computer Science, Information Systems</topic><topic>Data models</topic><topic>Deep learning</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Engineering</topic><topic>Engineering, Electrical & Electronic</topic><topic>Functional magnetic resonance imaging</topic><topic>Health care</topic><topic>improved deep learning algorithm (IDLA)</topic><topic>Machine learning</topic><topic>Multimedia</topic><topic>Neural networks</topic><topic>R-fMRI data</topic><topic>Science & Technology</topic><topic>Technology</topic><topic>Telecommunications</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Haibing</creatorcontrib><creatorcontrib>Zhang, Yongjin</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Xplore Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Web of Science - Science Citation Index Expanded - 2020</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Haibing</au><au>Zhang, Yongjin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer's Disease</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><stitle>IEEE ACCESS</stitle><date>2020</date><risdate>2020</risdate><volume>8</volume><spage>115383</spage><epage>115392</epage><pages>115383-115392</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>The development of computerized healthcare has been powered by diagnostic imaging and machine learning techniques. In particular, recent advances in deep learning have opened a new era in support of multimedia healthcare distribution. For earlier detection of Alzheimer's disease, the study suggested the Improved Deep Learning Algorithm (IDLA) and statistically significant text information. The specific information in clinical text includes the age, sex and genes of the person and apolipoprotein E; the brain function is established using resting-state functional data (MRI) for the measurement of connectivity in the brain regions. A specialized network of autoencoders is used in earlier diagnosis to distinguish between natural aging and disorder progression. The suggested approach incorporates effectively biased neural network functionality and allows a reliable Alzheimer's disease recognition. In comparison with conventional classifiers depends on time series R-fMRI results, the proposed deep learning algorithm has improved significantly and, in the best cases, the standard deviation reduced by 45%, indicating the forecast model is more reliable and efficient in relation to conventional methodologies. The work examines the benefits of improved deep learning algorithms from recognizing high-dimensional information in healthcare and can lead to the early diagnosis and prevention of Alzheimer's disease.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.3003424</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-7182-6016</orcidid><orcidid>https://orcid.org/0000-0002-7159-7778</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aging (natural) Algorithms Alzheimer's disease autoencoder network Brain Brain modeling Computer Science Computer Science, Information Systems Data models Deep learning Diagnosis Diagnostic systems Engineering Engineering, Electrical & Electronic Functional magnetic resonance imaging Health care improved deep learning algorithm (IDLA) Machine learning Multimedia Neural networks R-fMRI data Science & Technology Technology Telecommunications |
| title | Resting State fMRI and Improved Deep Learning Algorithm for Earlier Detection of Alzheimer's Disease |
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