Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis
Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable pr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kondo, Tadashi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© ISAROB 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Artificial life and robotics - Springer Japan, 1997, 22(2016), 1 vom: 21. Nov., Seite 1-9 |
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| Übergeordnetes Werk: |
volume:22 ; year:2016 ; number:1 ; day:21 ; month:11 ; pages:1-9 |
| Links: |
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| DOI / URN: |
10.1007/s10015-016-0337-y |
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OLC2051540241 |
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| 520 | |a Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. | ||
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10.1007/s10015-016-0337-y doi (DE-627)OLC2051540241 (DE-He213)s10015-016-0337-y-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISAROB 2016 Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. Deep neural network GMDH Medical image diagnosis Evolutionary computation Machine learning GMDH-type neural network Kondo, Sayaka aut Ueno, Junji aut Takao, Shoichiro aut Enthalten in Artificial life and robotics Springer Japan, 1997 22(2016), 1 vom: 21. Nov., Seite 1-9 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:22 year:2016 number:1 day:21 month:11 pages:1-9 https://doi.org/10.1007/s10015-016-0337-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 1 21 11 1-9 |
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10.1007/s10015-016-0337-y doi (DE-627)OLC2051540241 (DE-He213)s10015-016-0337-y-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISAROB 2016 Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. Deep neural network GMDH Medical image diagnosis Evolutionary computation Machine learning GMDH-type neural network Kondo, Sayaka aut Ueno, Junji aut Takao, Shoichiro aut Enthalten in Artificial life and robotics Springer Japan, 1997 22(2016), 1 vom: 21. Nov., Seite 1-9 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:22 year:2016 number:1 day:21 month:11 pages:1-9 https://doi.org/10.1007/s10015-016-0337-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 1 21 11 1-9 |
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10.1007/s10015-016-0337-y doi (DE-627)OLC2051540241 (DE-He213)s10015-016-0337-y-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISAROB 2016 Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. Deep neural network GMDH Medical image diagnosis Evolutionary computation Machine learning GMDH-type neural network Kondo, Sayaka aut Ueno, Junji aut Takao, Shoichiro aut Enthalten in Artificial life and robotics Springer Japan, 1997 22(2016), 1 vom: 21. Nov., Seite 1-9 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:22 year:2016 number:1 day:21 month:11 pages:1-9 https://doi.org/10.1007/s10015-016-0337-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 1 21 11 1-9 |
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10.1007/s10015-016-0337-y doi (DE-627)OLC2051540241 (DE-He213)s10015-016-0337-y-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISAROB 2016 Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. Deep neural network GMDH Medical image diagnosis Evolutionary computation Machine learning GMDH-type neural network Kondo, Sayaka aut Ueno, Junji aut Takao, Shoichiro aut Enthalten in Artificial life and robotics Springer Japan, 1997 22(2016), 1 vom: 21. Nov., Seite 1-9 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:22 year:2016 number:1 day:21 month:11 pages:1-9 https://doi.org/10.1007/s10015-016-0337-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 1 21 11 1-9 |
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10.1007/s10015-016-0337-y doi (DE-627)OLC2051540241 (DE-He213)s10015-016-0337-y-p DE-627 ger DE-627 rakwb eng 004 VZ Kondo, Tadashi verfasserin aut Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ISAROB 2016 Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. Deep neural network GMDH Medical image diagnosis Evolutionary computation Machine learning GMDH-type neural network Kondo, Sayaka aut Ueno, Junji aut Takao, Shoichiro aut Enthalten in Artificial life and robotics Springer Japan, 1997 22(2016), 1 vom: 21. Nov., Seite 1-9 (DE-627)240152476 (DE-600)1413537-1 (DE-576)065025393 1433-5298 nnns volume:22 year:2016 number:1 day:21 month:11 pages:1-9 https://doi.org/10.1007/s10015-016-0337-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 22 2016 1 21 11 1-9 |
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Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis |
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Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. © ISAROB 2016 |
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Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. © ISAROB 2016 |
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Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized. © ISAROB 2016 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2051540241</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502155401.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10015-016-0337-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2051540241</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10015-016-0337-y-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kondo, Tadashi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Medical image diagnosis of kidney regions by deep feedback GMDH-type neural network using principal component-regression analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© ISAROB 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The deep feedback Group Method of Data Handling (GMDH)-type neural network is applied to the medical image recognition of kidney regions. In this algorithm, the principal component-regression analysis is used for the learning calculation of the neural network, and the accurate and stable predicted values are obtained. The neural network architecture is automatically organized so as to fit the complexity of the medical images using the prediction error criterion defined as Akaike’s Information Criterion (AIC) or Prediction Sum of Squares (PSS). The recognition results show that the deep feedback GMDH-type neural network algorithm is useful for the medical image recognition of kidney regions, because the optimum neural network architecture is automatically organized.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GMDH</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medical image diagnosis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary computation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GMDH-type neural network</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kondo, Sayaka</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ueno, Junji</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Takao, Shoichiro</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">22(2016), 1 vom: 21. 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