On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases

Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgen...
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Autor*in:	Karpov, A. V. [verfasserIn] Kozik, V. I. Nezhevenko, E. S. Schwartz, Y. Sh.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Anmerkung:	© Allerton Press, Inc. 2022

Übergeordnetes Werk:	Enthalten in: Optoelectronics, instrumentation and data processing - New York, NY : Allerton Press, 2007, 58(2022), 5 vom: Okt., Seite 487-494
Übergeordnetes Werk:	volume:58 ; year:2022 ; number:5 ; month:10 ; pages:487-494

Links:	Volltext

DOI / URN:	10.3103/S8756699022050065

Katalog-ID:	SPR051522136

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520			\|a Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different.
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700	1		\|a Schwartz, Y. Sh. \|4 aut
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allfields	10.3103/S8756699022050065 doi (DE-627)SPR051522136 (SPR)S8756699022050065-e DE-627 ger DE-627 rakwb eng Karpov, A. V. verfasserin aut On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022 Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. Kozik, V. I. aut Nezhevenko, E. S. aut Schwartz, Y. Sh. aut Enthalten in Optoelectronics, instrumentation and data processing New York, NY : Allerton Press, 2007 58(2022), 5 vom: Okt., Seite 487-494 (DE-627)531199916 (DE-600)2322600-6 1934-7944 nnns volume:58 year:2022 number:5 month:10 pages:487-494 https://dx.doi.org/10.3103/S8756699022050065 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_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 58 2022 5 10 487-494
spelling	10.3103/S8756699022050065 doi (DE-627)SPR051522136 (SPR)S8756699022050065-e DE-627 ger DE-627 rakwb eng Karpov, A. V. verfasserin aut On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022 Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. Kozik, V. I. aut Nezhevenko, E. S. aut Schwartz, Y. Sh. aut Enthalten in Optoelectronics, instrumentation and data processing New York, NY : Allerton Press, 2007 58(2022), 5 vom: Okt., Seite 487-494 (DE-627)531199916 (DE-600)2322600-6 1934-7944 nnns volume:58 year:2022 number:5 month:10 pages:487-494 https://dx.doi.org/10.3103/S8756699022050065 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_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 58 2022 5 10 487-494
allfields_unstemmed	10.3103/S8756699022050065 doi (DE-627)SPR051522136 (SPR)S8756699022050065-e DE-627 ger DE-627 rakwb eng Karpov, A. V. verfasserin aut On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022 Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. Kozik, V. I. aut Nezhevenko, E. S. aut Schwartz, Y. Sh. aut Enthalten in Optoelectronics, instrumentation and data processing New York, NY : Allerton Press, 2007 58(2022), 5 vom: Okt., Seite 487-494 (DE-627)531199916 (DE-600)2322600-6 1934-7944 nnns volume:58 year:2022 number:5 month:10 pages:487-494 https://dx.doi.org/10.3103/S8756699022050065 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_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 58 2022 5 10 487-494
allfieldsGer	10.3103/S8756699022050065 doi (DE-627)SPR051522136 (SPR)S8756699022050065-e DE-627 ger DE-627 rakwb eng Karpov, A. V. verfasserin aut On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022 Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. Kozik, V. I. aut Nezhevenko, E. S. aut Schwartz, Y. Sh. aut Enthalten in Optoelectronics, instrumentation and data processing New York, NY : Allerton Press, 2007 58(2022), 5 vom: Okt., Seite 487-494 (DE-627)531199916 (DE-600)2322600-6 1934-7944 nnns volume:58 year:2022 number:5 month:10 pages:487-494 https://dx.doi.org/10.3103/S8756699022050065 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_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 58 2022 5 10 487-494
allfieldsSound	10.3103/S8756699022050065 doi (DE-627)SPR051522136 (SPR)S8756699022050065-e DE-627 ger DE-627 rakwb eng Karpov, A. V. verfasserin aut On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Allerton Press, Inc. 2022 Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. Kozik, V. I. aut Nezhevenko, E. S. aut Schwartz, Y. Sh. aut Enthalten in Optoelectronics, instrumentation and data processing New York, NY : Allerton Press, 2007 58(2022), 5 vom: Okt., Seite 487-494 (DE-627)531199916 (DE-600)2322600-6 1934-7944 nnns volume:58 year:2022 number:5 month:10 pages:487-494 https://dx.doi.org/10.3103/S8756699022050065 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_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 58 2022 5 10 487-494
language	English
source	Enthalten in Optoelectronics, instrumentation and data processing 58(2022), 5 vom: Okt., Seite 487-494 volume:58 year:2022 number:5 month:10 pages:487-494
sourceStr	Enthalten in Optoelectronics, instrumentation and data processing 58(2022), 5 vom: Okt., Seite 487-494 volume:58 year:2022 number:5 month:10 pages:487-494
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container_title	Optoelectronics, instrumentation and data processing
authorswithroles_txt_mv	Karpov, A. V. @@aut@@ Kozik, V. I. @@aut@@ Nezhevenko, E. S. @@aut@@ Schwartz, Y. Sh. @@aut@@
publishDateDaySort_date	2022-10-01T00:00:00Z
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author	Karpov, A. V.
spellingShingle	Karpov, A. V. On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases
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topic_title	On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases
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title	On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases
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title_full	On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases
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author_browse	Karpov, A. V. Kozik, V. I. Nezhevenko, E. S. Schwartz, Y. Sh.
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doi_str_mv	10.3103/S8756699022050065
title_sort	on the influence of the quality of databases of x-ray images of patients with tuberculosis on the diagnostics of deceases
title_auth	On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases
abstract	Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. © Allerton Press, Inc. 2022
abstractGer	Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. © Allerton Press, Inc. 2022
abstract_unstemmed	Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different. © Allerton Press, Inc. 2022
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title_short	On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases
url	https://dx.doi.org/10.3103/S8756699022050065
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author2	Kozik, V. I. Nezhevenko, E. S. Schwartz, Y. Sh
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V.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Allerton Press, Inc. 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The reliability of the training samples used for training convolutional neural networks applied for diagnostics of lung diseases has been investigated. By the example of the Kaggle database, it has been shown that the sample containing 3500 roentgenograms of healthy people and 3500 roentgenograms of patients with tuberculosis is very inhomogeneous. When training by using different parts of the sample and recognizing its different parts, the results obtained are significantly different.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kozik, V. I.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nezhevenko, E. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schwartz, Y. Sh.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Optoelectronics, instrumentation and data processing</subfield><subfield code="d">New York, NY : Allerton Press, 2007</subfield><subfield code="g">58(2022), 5 vom: Okt., Seite 487-494</subfield><subfield code="w">(DE-627)531199916</subfield><subfield code="w">(DE-600)2322600-6</subfield><subfield code="x">1934-7944</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:58</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:5</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:487-494</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.3103/S8756699022050065</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield 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On the Influence of the Quality of Databases of X-Ray Images of Patients with Tuberculosis on the Diagnostics of Deceases

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