A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential...
Gespeichert in:
| Veröffentlicht in: | Journal of medical systems 2019-09, Vol.43 (9), p.299-9, Article 299 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 9 |
|---|---|
| container_issue | 9 |
| container_start_page | 299 |
| container_title | Journal of medical systems |
| container_volume | 43 |
| creator | Raghavendra, U. Gudigar, Anjan Bhandary, Sulatha V. Rao, Tejaswi N. Ciaccio, Edward J. Acharya, U. Rajendra |
| description | Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an
F
−
measure
value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions. |
| doi_str_mv | 10.1007/s10916-019-1427-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2267022683</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2267022683</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-b69f22f9c0ab74cc50a9f309aa0bbf71e812c322c222a641daaf55bda9f81eca3</originalsourceid><addsrcrecordid>eNp1kE1LAzEURYMotlZ_gBsJuHEzmo_5yrIUWwsVF1ZwF95kkjqlM6nJDG3_vSmtCoKbBJJz73schK4puaeEZA-eEkHTiFAR0Zhl0fYE9WmS8SjNxfsp6hMa51GSiLyHLrxfEkJEmmbnqMcpTwTjpI-eh3i-sXgGO-3w6xqc13jYtVY3ypbhyViHJyvolK0BT0vdtJWpFLSVbfCmaj_wuGvKzuNpDQvtL9GZgZXXV8d7gN7Gj_PRUzR7mUxHw1mkeMbaqEiFYcwIRaDIYqUSAsJwIgBIUZiM6pwyxRlTjDFIY1oCmCQpykDlVCvgA3R36F07-9lp38q68kqvVtBo23nJWJqRcOQ8oLd_0KXtXBO221Mpi-OcxoGiB0o5673TRq5dVYPbSUrk3rU8uJbBtdy7ltuQuTk2d0Wty5_Et9wAsAPgw1ez0O539P-tX5Sjic0</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2266244814</pqid></control><display><type>article</type><title>A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Raghavendra, U. ; Gudigar, Anjan ; Bhandary, Sulatha V. ; Rao, Tejaswi N. ; Ciaccio, Edward J. ; Acharya, U. Rajendra</creator><creatorcontrib>Raghavendra, U. ; Gudigar, Anjan ; Bhandary, Sulatha V. ; Rao, Tejaswi N. ; Ciaccio, Edward J. ; Acharya, U. Rajendra</creatorcontrib><description>Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an
F
−
measure
value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.</description><identifier>ISSN: 0148-5598</identifier><identifier>EISSN: 1573-689X</identifier><identifier>DOI: 10.1007/s10916-019-1427-x</identifier><identifier>PMID: 31359230</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Adult ; Algorithms ; Case-Control Studies ; Diagnostic Techniques, Ophthalmological ; Digital imaging ; Female ; Fundus Oculi ; Glaucoma ; Health Informatics ; Health Informatics and Computer Vision ; Health Sciences ; Humans ; Image & Signal Processing ; Image Interpretation, Computer-Assisted - methods ; Intraocular pressure ; Learning algorithms ; Machine learning ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Pattern Recognition, Automated - methods ; Quantitative analysis ; Recent Advances in Deep Learning for Biomedical Signal Processing ; Statistics for Life Sciences ; Test procedures ; Tomography, Optical Coherence - methods ; Vision</subject><ispartof>Journal of medical systems, 2019-09, Vol.43 (9), p.299-9, Article 299</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-b69f22f9c0ab74cc50a9f309aa0bbf71e812c322c222a641daaf55bda9f81eca3</citedby><cites>FETCH-LOGICAL-c372t-b69f22f9c0ab74cc50a9f309aa0bbf71e812c322c222a641daaf55bda9f81eca3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10916-019-1427-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10916-019-1427-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41466,42535,51296</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31359230$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Raghavendra, U.</creatorcontrib><creatorcontrib>Gudigar, Anjan</creatorcontrib><creatorcontrib>Bhandary, Sulatha V.</creatorcontrib><creatorcontrib>Rao, Tejaswi N.</creatorcontrib><creatorcontrib>Ciaccio, Edward J.</creatorcontrib><creatorcontrib>Acharya, U. Rajendra</creatorcontrib><title>A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images</title><title>Journal of medical systems</title><addtitle>J Med Syst</addtitle><addtitle>J Med Syst</addtitle><description>Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an
F
−
measure
value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Case-Control Studies</subject><subject>Diagnostic Techniques, Ophthalmological</subject><subject>Digital imaging</subject><subject>Female</subject><subject>Fundus Oculi</subject><subject>Glaucoma</subject><subject>Health Informatics</subject><subject>Health Informatics and Computer Vision</subject><subject>Health Sciences</subject><subject>Humans</subject><subject>Image & Signal Processing</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Intraocular pressure</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Quantitative analysis</subject><subject>Recent Advances in Deep Learning for Biomedical Signal Processing</subject><subject>Statistics for Life Sciences</subject><subject>Test procedures</subject><subject>Tomography, Optical Coherence - methods</subject><subject>Vision</subject><issn>0148-5598</issn><issn>1573-689X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kE1LAzEURYMotlZ_gBsJuHEzmo_5yrIUWwsVF1ZwF95kkjqlM6nJDG3_vSmtCoKbBJJz73schK4puaeEZA-eEkHTiFAR0Zhl0fYE9WmS8SjNxfsp6hMa51GSiLyHLrxfEkJEmmbnqMcpTwTjpI-eh3i-sXgGO-3w6xqc13jYtVY3ypbhyViHJyvolK0BT0vdtJWpFLSVbfCmaj_wuGvKzuNpDQvtL9GZgZXXV8d7gN7Gj_PRUzR7mUxHw1mkeMbaqEiFYcwIRaDIYqUSAsJwIgBIUZiM6pwyxRlTjDFIY1oCmCQpykDlVCvgA3R36F07-9lp38q68kqvVtBo23nJWJqRcOQ8oLd_0KXtXBO221Mpi-OcxoGiB0o5673TRq5dVYPbSUrk3rU8uJbBtdy7ltuQuTk2d0Wty5_Et9wAsAPgw1ez0O539P-tX5Sjic0</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Raghavendra, U.</creator><creator>Gudigar, Anjan</creator><creator>Bhandary, Sulatha V.</creator><creator>Rao, Tejaswi N.</creator><creator>Ciaccio, Edward J.</creator><creator>Acharya, U. Rajendra</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7RV</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>KR7</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20190901</creationdate><title>A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images</title><author>Raghavendra, U. ; Gudigar, Anjan ; Bhandary, Sulatha V. ; Rao, Tejaswi N. ; Ciaccio, Edward J. ; Acharya, U. Rajendra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-b69f22f9c0ab74cc50a9f309aa0bbf71e812c322c222a641daaf55bda9f81eca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Case-Control Studies</topic><topic>Diagnostic Techniques, Ophthalmological</topic><topic>Digital imaging</topic><topic>Female</topic><topic>Fundus Oculi</topic><topic>Glaucoma</topic><topic>Health Informatics</topic><topic>Health Informatics and Computer Vision</topic><topic>Health Sciences</topic><topic>Humans</topic><topic>Image & Signal Processing</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Intraocular pressure</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Quantitative analysis</topic><topic>Recent Advances in Deep Learning for Biomedical Signal Processing</topic><topic>Statistics for Life Sciences</topic><topic>Test procedures</topic><topic>Tomography, Optical Coherence - methods</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Raghavendra, U.</creatorcontrib><creatorcontrib>Gudigar, Anjan</creatorcontrib><creatorcontrib>Bhandary, Sulatha V.</creatorcontrib><creatorcontrib>Rao, Tejaswi N.</creatorcontrib><creatorcontrib>Ciaccio, Edward J.</creatorcontrib><creatorcontrib>Acharya, U. Rajendra</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Biological Science Collection</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of medical systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Raghavendra, U.</au><au>Gudigar, Anjan</au><au>Bhandary, Sulatha V.</au><au>Rao, Tejaswi N.</au><au>Ciaccio, Edward J.</au><au>Acharya, U. Rajendra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images</atitle><jtitle>Journal of medical systems</jtitle><stitle>J Med Syst</stitle><addtitle>J Med Syst</addtitle><date>2019-09-01</date><risdate>2019</risdate><volume>43</volume><issue>9</issue><spage>299</spage><epage>9</epage><pages>299-9</pages><artnum>299</artnum><issn>0148-5598</issn><eissn>1573-689X</eissn><abstract>Glaucoma is a type of eye condition which may result in partial or consummate vision loss. Higher intraocular pressure is the leading cause for this condition. Screening for glaucoma and early detection can avert vision loss. Computer aided diagnosis (CAD) is an automated process with the potential to identify glaucoma early through quantitative analysis of digital fundus images. Preparing an effective model for CAD requires a large database. This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach. An autoencoder is trained to determine effective and important features from fundus images. These features are used to develop classes of glaucoma for testing. The method achieved an
F
−
measure
value of 0.95 utilizing 1426 digital fundus images (589 control and 837 glaucoma). The efficacy of the system is evident, and is suggestive of its possible utility as an additional tool for verification of clinical decisions.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>31359230</pmid><doi>10.1007/s10916-019-1427-x</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0148-5598 |
| ispartof | Journal of medical systems, 2019-09, Vol.43 (9), p.299-9, Article 299 |
| issn | 0148-5598 1573-689X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2267022683 |
| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Adult Algorithms Case-Control Studies Diagnostic Techniques, Ophthalmological Digital imaging Female Fundus Oculi Glaucoma Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image Interpretation, Computer-Assisted - methods Intraocular pressure Learning algorithms Machine learning Male Medicine Medicine & Public Health Middle Aged Pattern Recognition, Automated - methods Quantitative analysis Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Test procedures Tomography, Optical Coherence - methods Vision |
| title | A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T09%3A12%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Two%20Layer%20Sparse%20Autoencoder%20for%20Glaucoma%20Identification%20with%20Fundus%20Images&rft.jtitle=Journal%20of%20medical%20systems&rft.au=Raghavendra,%20U.&rft.date=2019-09-01&rft.volume=43&rft.issue=9&rft.spage=299&rft.epage=9&rft.pages=299-9&rft.artnum=299&rft.issn=0148-5598&rft.eissn=1573-689X&rft_id=info:doi/10.1007/s10916-019-1427-x&rft_dat=%3Cproquest_cross%3E2267022683%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2266244814&rft_id=info:pmid/31359230&rfr_iscdi=true |