Application of artificial neural networks for the estimation of tumour characteristics in biological tissues
Background Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce ‘forward results’. Methods Three feed‐forward neural networks (FFNN) have been developed for the estimation of tumour characteri...
Gespeichert in:
| Veröffentlicht in: | The international journal of medical robotics + computer assisted surgery 2007-09, Vol.3 (3), p.235-244 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 244 |
|---|---|
| container_issue | 3 |
| container_start_page | 235 |
| container_title | The international journal of medical robotics + computer assisted surgery |
| container_volume | 3 |
| creator | Hosseini, Seyed Mohsen Amiri, Mahmood Najarian, Siamak Dargahi, Javad |
| description | Background
Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce ‘forward results’.
Methods
Three feed‐forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back‐propagation (RP) algorithm with a leave‐one‐out (LOO) cross‐validation approach is used for training purposes.
Results
The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.
Conclusion
There is a non‐linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small. Copyright © 2007 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/rcs.138 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_68385775</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>30967629</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3848-34db9ca94b4ab90fd4f10f7caee6ef2e0369d388a233771faf87c88f0937b3403</originalsourceid><addsrcrecordid>eNqFkctOLCEURYm5xnf8gxtGOjClUFAFDE3HV-Ij8T0jFA1XlG5aoKL-vWh12pG5o0NyFuskewOwjdE-Rqg-iDrtY8KXwBqmjFeNaB__LN4NXgXrKT0jRBva0hWwilnDGBd4DfjD2cw7rbILUxgsVDE767RTHk5NH79HfgvxJUEbIsxPBpqU3WTxIfeT0Eeon1RUOpvoylYn6Kawc8GHf8XtYXYp9SZtgmWrfDJb87kB7o6Pbken1fnVydno8LzShFNeETruhFaCdlR1AtkxtRhZppUxrbG1QaQVY8K5qglhDFtlOdOcWyQI6whFZAPsDN5ZDK_lbpYTl7TxXk1N6JNsOeElgOa_IEGiZW0tCrg7gDqGlKKxchZLCPFDYiS_GpClAVkaKOTfubLvJmb8w80jL8DeALw5bz5-88jr0c2gqwa65GreF7SKL7JlhDXy4fJEnta0ubiviaTkEwNgoIk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>30967629</pqid></control><display><type>article</type><title>Application of artificial neural networks for the estimation of tumour characteristics in biological tissues</title><source>MEDLINE</source><source>Wiley Online Library All Journals</source><creator>Hosseini, Seyed Mohsen ; Amiri, Mahmood ; Najarian, Siamak ; Dargahi, Javad</creator><creatorcontrib>Hosseini, Seyed Mohsen ; Amiri, Mahmood ; Najarian, Siamak ; Dargahi, Javad</creatorcontrib><description>Background
Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce ‘forward results’.
Methods
Three feed‐forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back‐propagation (RP) algorithm with a leave‐one‐out (LOO) cross‐validation approach is used for training purposes.
Results
The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.
Conclusion
There is a non‐linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small. Copyright © 2007 John Wiley & Sons, Ltd.</description><identifier>ISSN: 1478-5951</identifier><identifier>EISSN: 1478-596X</identifier><identifier>DOI: 10.1002/rcs.138</identifier><identifier>PMID: 17577891</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Algorithms ; artificial neural network ; Artificial tactile sensing ; Computer Simulation ; Diagnosis, Computer-Assisted - methods ; Humans ; inverse solution ; Models, Biological ; Neoplasms - diagnosis ; Neoplasms - physiopathology ; Neural Networks (Computer) ; Palpation - methods ; Pattern Recognition, Automated - methods</subject><ispartof>The international journal of medical robotics + computer assisted surgery, 2007-09, Vol.3 (3), p.235-244</ispartof><rights>Copyright © 2007 John Wiley & Sons, Ltd.</rights><rights>2007 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3848-34db9ca94b4ab90fd4f10f7caee6ef2e0369d388a233771faf87c88f0937b3403</citedby><cites>FETCH-LOGICAL-c3848-34db9ca94b4ab90fd4f10f7caee6ef2e0369d388a233771faf87c88f0937b3403</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Frcs.138$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Frcs.138$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27922,27923,45572,45573</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17577891$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hosseini, Seyed Mohsen</creatorcontrib><creatorcontrib>Amiri, Mahmood</creatorcontrib><creatorcontrib>Najarian, Siamak</creatorcontrib><creatorcontrib>Dargahi, Javad</creatorcontrib><title>Application of artificial neural networks for the estimation of tumour characteristics in biological tissues</title><title>The international journal of medical robotics + computer assisted surgery</title><addtitle>Int. J. Med. Robotics Comput. Assist. Surg</addtitle><description>Background
Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce ‘forward results’.
Methods
Three feed‐forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back‐propagation (RP) algorithm with a leave‐one‐out (LOO) cross‐validation approach is used for training purposes.
Results
The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.
Conclusion
There is a non‐linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small. Copyright © 2007 John Wiley & Sons, Ltd.</description><subject>Algorithms</subject><subject>artificial neural network</subject><subject>Artificial tactile sensing</subject><subject>Computer Simulation</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Humans</subject><subject>inverse solution</subject><subject>Models, Biological</subject><subject>Neoplasms - diagnosis</subject><subject>Neoplasms - physiopathology</subject><subject>Neural Networks (Computer)</subject><subject>Palpation - methods</subject><subject>Pattern Recognition, Automated - methods</subject><issn>1478-5951</issn><issn>1478-596X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkctOLCEURYm5xnf8gxtGOjClUFAFDE3HV-Ij8T0jFA1XlG5aoKL-vWh12pG5o0NyFuskewOwjdE-Rqg-iDrtY8KXwBqmjFeNaB__LN4NXgXrKT0jRBva0hWwilnDGBd4DfjD2cw7rbILUxgsVDE767RTHk5NH79HfgvxJUEbIsxPBpqU3WTxIfeT0Eeon1RUOpvoylYn6Kawc8GHf8XtYXYp9SZtgmWrfDJb87kB7o6Pbken1fnVydno8LzShFNeETruhFaCdlR1AtkxtRhZppUxrbG1QaQVY8K5qglhDFtlOdOcWyQI6whFZAPsDN5ZDK_lbpYTl7TxXk1N6JNsOeElgOa_IEGiZW0tCrg7gDqGlKKxchZLCPFDYiS_GpClAVkaKOTfubLvJmb8w80jL8DeALw5bz5-88jr0c2gqwa65GreF7SKL7JlhDXy4fJEnta0ubiviaTkEwNgoIk</recordid><startdate>200709</startdate><enddate>200709</enddate><creator>Hosseini, Seyed Mohsen</creator><creator>Amiri, Mahmood</creator><creator>Najarian, Siamak</creator><creator>Dargahi, Javad</creator><general>John Wiley & Sons, Ltd</general><scope>BSCLL</scope><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>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope></search><sort><creationdate>200709</creationdate><title>Application of artificial neural networks for the estimation of tumour characteristics in biological tissues</title><author>Hosseini, Seyed Mohsen ; Amiri, Mahmood ; Najarian, Siamak ; Dargahi, Javad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3848-34db9ca94b4ab90fd4f10f7caee6ef2e0369d388a233771faf87c88f0937b3403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>artificial neural network</topic><topic>Artificial tactile sensing</topic><topic>Computer Simulation</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Humans</topic><topic>inverse solution</topic><topic>Models, Biological</topic><topic>Neoplasms - diagnosis</topic><topic>Neoplasms - physiopathology</topic><topic>Neural Networks (Computer)</topic><topic>Palpation - methods</topic><topic>Pattern Recognition, Automated - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hosseini, Seyed Mohsen</creatorcontrib><creatorcontrib>Amiri, Mahmood</creatorcontrib><creatorcontrib>Najarian, Siamak</creatorcontrib><creatorcontrib>Dargahi, Javad</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering 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>MEDLINE - Academic</collection><jtitle>The international journal of medical robotics + computer assisted surgery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hosseini, Seyed Mohsen</au><au>Amiri, Mahmood</au><au>Najarian, Siamak</au><au>Dargahi, Javad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of artificial neural networks for the estimation of tumour characteristics in biological tissues</atitle><jtitle>The international journal of medical robotics + computer assisted surgery</jtitle><addtitle>Int. J. Med. Robotics Comput. Assist. Surg</addtitle><date>2007-09</date><risdate>2007</risdate><volume>3</volume><issue>3</issue><spage>235</spage><epage>244</epage><pages>235-244</pages><issn>1478-5951</issn><eissn>1478-596X</eissn><abstract>Background
Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce ‘forward results’.
Methods
Three feed‐forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back‐propagation (RP) algorithm with a leave‐one‐out (LOO) cross‐validation approach is used for training purposes.
Results
The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.
Conclusion
There is a non‐linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small. Copyright © 2007 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>17577891</pmid><doi>10.1002/rcs.138</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1478-5951 |
| ispartof | The international journal of medical robotics + computer assisted surgery, 2007-09, Vol.3 (3), p.235-244 |
| issn | 1478-5951 1478-596X |
| language | eng |
| recordid | cdi_proquest_miscellaneous_68385775 |
| source | MEDLINE; Wiley Online Library All Journals |
| subjects | Algorithms artificial neural network Artificial tactile sensing Computer Simulation Diagnosis, Computer-Assisted - methods Humans inverse solution Models, Biological Neoplasms - diagnosis Neoplasms - physiopathology Neural Networks (Computer) Palpation - methods Pattern Recognition, Automated - methods |
| title | Application of artificial neural networks for the estimation of tumour characteristics in biological tissues |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T22%3A27%3A44IST&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=Application%20of%20artificial%20neural%20networks%20for%20the%20estimation%20of%20tumour%20characteristics%20in%20biological%20tissues&rft.jtitle=The%20international%20journal%20of%20medical%20robotics%20+%20computer%20assisted%20surgery&rft.au=Hosseini,%20Seyed%20Mohsen&rft.date=2007-09&rft.volume=3&rft.issue=3&rft.spage=235&rft.epage=244&rft.pages=235-244&rft.issn=1478-5951&rft.eissn=1478-596X&rft_id=info:doi/10.1002/rcs.138&rft_dat=%3Cproquest_cross%3E30967629%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=30967629&rft_id=info:pmid/17577891&rfr_iscdi=true |