Lung diseases identification method based on capsule neural network

Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture fe...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhao, Di [verfasserIn] Liu, Jing Zhou, Guo-Xiong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Capsule neural network Image preprocessing Data enhancement Lung diseases identification

Anmerkung:	© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Übergeordnetes Werk:	Enthalten in: Evolutionary intelligence - Berlin : Springer, 2008, 15(2020), 4 vom: 19. Juni, Seite 2375-2384
Übergeordnetes Werk:	volume:15 ; year:2020 ; number:4 ; day:19 ; month:06 ; pages:2375-2384

Links:	Volltext

DOI / URN:	10.1007/s12065-020-00408-6

Katalog-ID:	SPR048474738

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520			\|a Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases.
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allfields	10.1007/s12065-020-00408-6 doi (DE-627)SPR048474738 (SPR)s12065-020-00408-6-e DE-627 ger DE-627 rakwb eng Zhao, Di verfasserin aut Lung diseases identification method based on capsule neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. Capsule neural network (dpeaa)DE-He213 Image preprocessing (dpeaa)DE-He213 Data enhancement (dpeaa)DE-He213 Lung diseases identification (dpeaa)DE-He213 Liu, Jing aut Zhou, Guo-Xiong aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 15(2020), 4 vom: 19. Juni, Seite 2375-2384 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:15 year:2020 number:4 day:19 month:06 pages:2375-2384 https://dx.doi.org/10.1007/s12065-020-00408-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2020 4 19 06 2375-2384
spelling	10.1007/s12065-020-00408-6 doi (DE-627)SPR048474738 (SPR)s12065-020-00408-6-e DE-627 ger DE-627 rakwb eng Zhao, Di verfasserin aut Lung diseases identification method based on capsule neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. Capsule neural network (dpeaa)DE-He213 Image preprocessing (dpeaa)DE-He213 Data enhancement (dpeaa)DE-He213 Lung diseases identification (dpeaa)DE-He213 Liu, Jing aut Zhou, Guo-Xiong aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 15(2020), 4 vom: 19. Juni, Seite 2375-2384 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:15 year:2020 number:4 day:19 month:06 pages:2375-2384 https://dx.doi.org/10.1007/s12065-020-00408-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2020 4 19 06 2375-2384
allfields_unstemmed	10.1007/s12065-020-00408-6 doi (DE-627)SPR048474738 (SPR)s12065-020-00408-6-e DE-627 ger DE-627 rakwb eng Zhao, Di verfasserin aut Lung diseases identification method based on capsule neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. Capsule neural network (dpeaa)DE-He213 Image preprocessing (dpeaa)DE-He213 Data enhancement (dpeaa)DE-He213 Lung diseases identification (dpeaa)DE-He213 Liu, Jing aut Zhou, Guo-Xiong aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 15(2020), 4 vom: 19. Juni, Seite 2375-2384 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:15 year:2020 number:4 day:19 month:06 pages:2375-2384 https://dx.doi.org/10.1007/s12065-020-00408-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2020 4 19 06 2375-2384
allfieldsGer	10.1007/s12065-020-00408-6 doi (DE-627)SPR048474738 (SPR)s12065-020-00408-6-e DE-627 ger DE-627 rakwb eng Zhao, Di verfasserin aut Lung diseases identification method based on capsule neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. Capsule neural network (dpeaa)DE-He213 Image preprocessing (dpeaa)DE-He213 Data enhancement (dpeaa)DE-He213 Lung diseases identification (dpeaa)DE-He213 Liu, Jing aut Zhou, Guo-Xiong aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 15(2020), 4 vom: 19. Juni, Seite 2375-2384 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:15 year:2020 number:4 day:19 month:06 pages:2375-2384 https://dx.doi.org/10.1007/s12065-020-00408-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2020 4 19 06 2375-2384
allfieldsSound	10.1007/s12065-020-00408-6 doi (DE-627)SPR048474738 (SPR)s12065-020-00408-6-e DE-627 ger DE-627 rakwb eng Zhao, Di verfasserin aut Lung diseases identification method based on capsule neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. Capsule neural network (dpeaa)DE-He213 Image preprocessing (dpeaa)DE-He213 Data enhancement (dpeaa)DE-He213 Lung diseases identification (dpeaa)DE-He213 Liu, Jing aut Zhou, Guo-Xiong aut Enthalten in Evolutionary intelligence Berlin : Springer, 2008 15(2020), 4 vom: 19. Juni, Seite 2375-2384 (DE-627)566007215 (DE-600)2424716-9 1864-5917 nnns volume:15 year:2020 number:4 day:19 month:06 pages:2375-2384 https://dx.doi.org/10.1007/s12065-020-00408-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_101 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2020 4 19 06 2375-2384
language	English
source	Enthalten in Evolutionary intelligence 15(2020), 4 vom: 19. Juni, Seite 2375-2384 volume:15 year:2020 number:4 day:19 month:06 pages:2375-2384
sourceStr	Enthalten in Evolutionary intelligence 15(2020), 4 vom: 19. Juni, Seite 2375-2384 volume:15 year:2020 number:4 day:19 month:06 pages:2375-2384
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Capsule neural network Image preprocessing Data enhancement Lung diseases identification
isfreeaccess_bool	false
container_title	Evolutionary intelligence
authorswithroles_txt_mv	Zhao, Di @@aut@@ Liu, Jing @@aut@@ Zhou, Guo-Xiong @@aut@@
publishDateDaySort_date	2020-06-19T00:00:00Z
hierarchy_top_id	566007215
id	SPR048474738
language_de	englisch
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author	Zhao, Di
spellingShingle	Zhao, Di misc Capsule neural network misc Image preprocessing misc Data enhancement misc Lung diseases identification Lung diseases identification method based on capsule neural network
authorStr	Zhao, Di
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topic_title	Lung diseases identification method based on capsule neural network Capsule neural network (dpeaa)DE-He213 Image preprocessing (dpeaa)DE-He213 Data enhancement (dpeaa)DE-He213 Lung diseases identification (dpeaa)DE-He213
topic	misc Capsule neural network misc Image preprocessing misc Data enhancement misc Lung diseases identification
topic_unstemmed	misc Capsule neural network misc Image preprocessing misc Data enhancement misc Lung diseases identification
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title	Lung diseases identification method based on capsule neural network
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title_full	Lung diseases identification method based on capsule neural network
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doi_str_mv	10.1007/s12065-020-00408-6
title_sort	lung diseases identification method based on capsule neural network
title_auth	Lung diseases identification method based on capsule neural network
abstract	Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. © Springer-Verlag GmbH Germany, part of Springer Nature 2020
abstractGer	Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. © Springer-Verlag GmbH Germany, part of Springer Nature 2020
abstract_unstemmed	Abstract In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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container_issue	4
title_short	Lung diseases identification method based on capsule neural network
url	https://dx.doi.org/10.1007/s12065-020-00408-6
remote_bool	true
author2	Liu, Jing Zhou, Guo-Xiong
author2Str	Liu, Jing Zhou, Guo-Xiong
ppnlink	566007215
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s12065-020-00408-6
up_date	2024-07-03T19:27:20.066Z
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Lung diseases identification method based on capsule neural network

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