Medical image diagnosis of lung cancer by a revised GMDH-type neural network using various kinds of neuron
Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neura...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kondo, Tadashi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Anmerkung: |
© International Symposium on Artificial Life and Robotics (ISAROB). 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Artificial life and robotics - Springer Japan, 1997, 16(2011), 3 vom: Dez., Seite 301-306 |
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| Übergeordnetes Werk: |
volume:16 ; year:2011 ; number:3 ; month:12 ; pages:301-306 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10015-011-0936-6 |
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OLC2051536570 |
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| 520 | |a Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. | ||
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10.1007/s10015-011-0936-6 doi (DE-627)OLC2051536570 (DE-He213)s10015-011-0936-6-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of lung cancer by a revised GMDH-type neural network using various kinds of neuron 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Symposium on Artificial Life and Robotics (ISAROB). 2011 Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. Ueno, Junji aut Enthalten in Artificial life and robotics Springer Japan, 1997 16(2011), 3 vom: Dez., Seite 301-306 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:16 year:2011 number:3 month:12 pages:301-306 https://doi.org/10.1007/s10015-011-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4322 AR 16 2011 3 12 301-306 |
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10.1007/s10015-011-0936-6 doi (DE-627)OLC2051536570 (DE-He213)s10015-011-0936-6-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of lung cancer by a revised GMDH-type neural network using various kinds of neuron 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Symposium on Artificial Life and Robotics (ISAROB). 2011 Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. Ueno, Junji aut Enthalten in Artificial life and robotics Springer Japan, 1997 16(2011), 3 vom: Dez., Seite 301-306 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:16 year:2011 number:3 month:12 pages:301-306 https://doi.org/10.1007/s10015-011-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4322 AR 16 2011 3 12 301-306 |
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10.1007/s10015-011-0936-6 doi (DE-627)OLC2051536570 (DE-He213)s10015-011-0936-6-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of lung cancer by a revised GMDH-type neural network using various kinds of neuron 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Symposium on Artificial Life and Robotics (ISAROB). 2011 Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. Ueno, Junji aut Enthalten in Artificial life and robotics Springer Japan, 1997 16(2011), 3 vom: Dez., Seite 301-306 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:16 year:2011 number:3 month:12 pages:301-306 https://doi.org/10.1007/s10015-011-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4322 AR 16 2011 3 12 301-306 |
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10.1007/s10015-011-0936-6 doi (DE-627)OLC2051536570 (DE-He213)s10015-011-0936-6-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of lung cancer by a revised GMDH-type neural network using various kinds of neuron 2011 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © International Symposium on Artificial Life and Robotics (ISAROB). 2011 Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. Ueno, Junji aut Enthalten in Artificial life and robotics Springer Japan, 1997 16(2011), 3 vom: Dez., Seite 301-306 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:16 year:2011 number:3 month:12 pages:301-306 https://doi.org/10.1007/s10015-011-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4322 AR 16 2011 3 12 301-306 |
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Medical image diagnosis of lung cancer by a revised GMDH-type neural network using various kinds of neuron |
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Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. © International Symposium on Artificial Life and Robotics (ISAROB). 2011 |
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Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. © International Symposium on Artificial Life and Robotics (ISAROB). 2011 |
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Abstract A revised group method of data handling (GMDH)-type neural network algorithm using various kinds of neuron is applied to the medical image diagnosis of lung cancer. The optimum neural network architecture for medical image diagnosis is automatically organized using a revised GMDH-type neural network algorithm, and the regions of lung cancer are recognized and extracted accurately. In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system. © International Symposium on Artificial Life and Robotics (ISAROB). 2011 |
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In this revised GMDH-type neural network algorithm, polynomial-type and radial basis function (RBF)-type neurons are used for organizing the neural network architecture in order to fit the complexity of the nonlinear system.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ueno, Junji</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Artificial life and robotics</subfield><subfield code="d">Springer Japan, 1997</subfield><subfield code="g">16(2011), 3 vom: Dez., Seite 301-306</subfield><subfield code="w">(DE-627)240152476</subfield><subfield code="w">(DE-600)1413537-1</subfield><subfield code="w">(DE-576)065025393</subfield><subfield code="x">1433-5298</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:3</subfield><subfield code="g">month:12</subfield><subfield code="g">pages:301-306</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10015-011-0936-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2011</subfield><subfield code="e">3</subfield><subfield code="c">12</subfield><subfield code="h">301-306</subfield></datafield></record></collection>
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