Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm

Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet co...
Ausführliche Beschreibung

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Autor*in:	Qiao, Zhou [verfasserIn] Kewen, Xia Panpan, Wu Wang, Haoran

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	bat algorithm Curvelet transform PCA LDA NPE

Anmerkung:	© Pleiades Publishing, Ltd. 2017

Übergeordnetes Werk:	Enthalten in: Pattern recognition and image analysis - Moscow : MAIK Nauka/Interperiodica Publ., 1996, 27(2017), 4 vom: Okt., Seite 855-862
Übergeordnetes Werk:	volume:27 ; year:2017 ; number:4 ; month:10 ; pages:855-862

Links:	Volltext

DOI / URN:	10.1134/S1054661817040228

Katalog-ID:	SPR020182333

Internformat


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520			\|a Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher.
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650		4	\|a Curvelet transform \|7 (dpeaa)DE-He213
650		4	\|a PCA \|7 (dpeaa)DE-He213
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912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
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912			\|a GBV_ILN_2005
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912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
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912			\|a GBV_ILN_2031
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912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
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912			\|a GBV_ILN_2048
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912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
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912			\|a GBV_ILN_2113
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912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
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publishDate	2017
allfields	10.1134/S1054661817040228 doi (DE-627)SPR020182333 (SPR)S1054661817040228-e DE-627 ger DE-627 rakwb eng Qiao, Zhou verfasserin aut Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2017 Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. bat algorithm (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 PCA (dpeaa)DE-He213 LDA (dpeaa)DE-He213 NPE (dpeaa)DE-He213 Kewen, Xia aut Panpan, Wu aut Wang, Haoran aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 27(2017), 4 vom: Okt., Seite 855-862 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:27 year:2017 number:4 month:10 pages:855-862 https://dx.doi.org/10.1134/S1054661817040228 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2017 4 10 855-862
spelling	10.1134/S1054661817040228 doi (DE-627)SPR020182333 (SPR)S1054661817040228-e DE-627 ger DE-627 rakwb eng Qiao, Zhou verfasserin aut Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2017 Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. bat algorithm (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 PCA (dpeaa)DE-He213 LDA (dpeaa)DE-He213 NPE (dpeaa)DE-He213 Kewen, Xia aut Panpan, Wu aut Wang, Haoran aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 27(2017), 4 vom: Okt., Seite 855-862 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:27 year:2017 number:4 month:10 pages:855-862 https://dx.doi.org/10.1134/S1054661817040228 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2017 4 10 855-862
allfields_unstemmed	10.1134/S1054661817040228 doi (DE-627)SPR020182333 (SPR)S1054661817040228-e DE-627 ger DE-627 rakwb eng Qiao, Zhou verfasserin aut Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2017 Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. bat algorithm (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 PCA (dpeaa)DE-He213 LDA (dpeaa)DE-He213 NPE (dpeaa)DE-He213 Kewen, Xia aut Panpan, Wu aut Wang, Haoran aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 27(2017), 4 vom: Okt., Seite 855-862 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:27 year:2017 number:4 month:10 pages:855-862 https://dx.doi.org/10.1134/S1054661817040228 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2017 4 10 855-862
allfieldsGer	10.1134/S1054661817040228 doi (DE-627)SPR020182333 (SPR)S1054661817040228-e DE-627 ger DE-627 rakwb eng Qiao, Zhou verfasserin aut Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2017 Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. bat algorithm (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 PCA (dpeaa)DE-He213 LDA (dpeaa)DE-He213 NPE (dpeaa)DE-He213 Kewen, Xia aut Panpan, Wu aut Wang, Haoran aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 27(2017), 4 vom: Okt., Seite 855-862 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:27 year:2017 number:4 month:10 pages:855-862 https://dx.doi.org/10.1134/S1054661817040228 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2017 4 10 855-862
allfieldsSound	10.1134/S1054661817040228 doi (DE-627)SPR020182333 (SPR)S1054661817040228-e DE-627 ger DE-627 rakwb eng Qiao, Zhou verfasserin aut Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2017 Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. bat algorithm (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 PCA (dpeaa)DE-He213 LDA (dpeaa)DE-He213 NPE (dpeaa)DE-He213 Kewen, Xia aut Panpan, Wu aut Wang, Haoran aut Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 27(2017), 4 vom: Okt., Seite 855-862 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:27 year:2017 number:4 month:10 pages:855-862 https://dx.doi.org/10.1134/S1054661817040228 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2017 4 10 855-862
language	English
source	Enthalten in Pattern recognition and image analysis 27(2017), 4 vom: Okt., Seite 855-862 volume:27 year:2017 number:4 month:10 pages:855-862
sourceStr	Enthalten in Pattern recognition and image analysis 27(2017), 4 vom: Okt., Seite 855-862 volume:27 year:2017 number:4 month:10 pages:855-862
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	bat algorithm Curvelet transform PCA LDA NPE
isfreeaccess_bool	false
container_title	Pattern recognition and image analysis
authorswithroles_txt_mv	Qiao, Zhou @@aut@@ Kewen, Xia @@aut@@ Panpan, Wu @@aut@@ Wang, Haoran @@aut@@
publishDateDaySort_date	2017-10-01T00:00:00Z
hierarchy_top_id	327148179
id	SPR020182333
language_de	englisch
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author	Qiao, Zhou
spellingShingle	Qiao, Zhou misc bat algorithm misc Curvelet transform misc PCA misc LDA misc NPE Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm
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topic_title	Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm bat algorithm (dpeaa)DE-He213 Curvelet transform (dpeaa)DE-He213 PCA (dpeaa)DE-He213 LDA (dpeaa)DE-He213 NPE (dpeaa)DE-He213
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title	Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm
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title_full	Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm
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author_browse	Qiao, Zhou Kewen, Xia Panpan, Wu Wang, Haoran
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title_sort	lung nodule classification using curvelet transform, lda algorithm and bat-svm algorithm
title_auth	Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm
abstract	Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. © Pleiades Publishing, Ltd. 2017
abstractGer	Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. © Pleiades Publishing, Ltd. 2017
abstract_unstemmed	Abstract The lung nodule is the manifestation of lung cancer, which is of great significance for early detection and treatment. Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. The experimental results show that the proposed method is feasible and the recognition accuracy is higher. © Pleiades Publishing, Ltd. 2017
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title_short	Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm
url	https://dx.doi.org/10.1134/S1054661817040228
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up_date	2024-07-03T14:24:29.174Z
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Traditional feature extraction vectors mainly consist of intensity features, shape features and texture features. A method which combines low and high frequency Curvelet coefficients with the feature vectors based on the traditional features to make up for contour and texture feature in details is proposed; Because PCA lacks supervision function in dimensionality reduction of multi-class data, thus the LDA algorithm is further used to deal with classification labels; Commonly used parameters optimization algorithms in SVM are cross validation grid search, genetic algorithm and PSO algorithm. In this paper, the new smart bat algorithm is used for parameters optimization, making it simple and rapid. 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Lung nodule classification using curvelet transform, LDA algorithm and BAT-SVM algorithm

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