Automatic classification of pulmonary diseases using a structural co-occurrence matrix

Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be...
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Autor*in:	Peixoto, Solon Alves [verfasserIn] Filho, Pedro P. Rebouças [verfasserIn] Arun Kumar, N. [verfasserIn] de Albuquerque, Victor Hugo C. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Structural co-occurrence matrix Feature extraction Machine learning classifiers Medical image processing Lung disease

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 32(2018), 15 vom: 20. Sept., Seite 10935-10945
Übergeordnetes Werk:	volume:32 ; year:2018 ; number:15 ; day:20 ; month:09 ; pages:10935-10945

Links:	Volltext

DOI / URN:	10.1007/s00521-018-3736-2

Katalog-ID:	SPR040374831

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520			\|a Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision.
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700	1		\|a de Albuquerque, Victor Hugo C. \|e verfasserin \|4 aut
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allfields	10.1007/s00521-018-3736-2 doi (DE-627)SPR040374831 (SPR)s00521-018-3736-2-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Peixoto, Solon Alves verfasserin aut Automatic classification of pulmonary diseases using a structural co-occurrence matrix 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision. Structural co-occurrence matrix (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Machine learning classifiers (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Lung disease (dpeaa)DE-He213 Filho, Pedro P. Rebouças verfasserin aut Arun Kumar, N. verfasserin aut de Albuquerque, Victor Hugo C. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2018), 15 vom: 20. Sept., Seite 10935-10945 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2018 number:15 day:20 month:09 pages:10935-10945 https://dx.doi.org/10.1007/s00521-018-3736-2 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_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_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 54.72 ASE AR 32 2018 15 20 09 10935-10945
spelling	10.1007/s00521-018-3736-2 doi (DE-627)SPR040374831 (SPR)s00521-018-3736-2-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Peixoto, Solon Alves verfasserin aut Automatic classification of pulmonary diseases using a structural co-occurrence matrix 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision. Structural co-occurrence matrix (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Machine learning classifiers (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Lung disease (dpeaa)DE-He213 Filho, Pedro P. Rebouças verfasserin aut Arun Kumar, N. verfasserin aut de Albuquerque, Victor Hugo C. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2018), 15 vom: 20. Sept., Seite 10935-10945 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2018 number:15 day:20 month:09 pages:10935-10945 https://dx.doi.org/10.1007/s00521-018-3736-2 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_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_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 54.72 ASE AR 32 2018 15 20 09 10935-10945
allfields_unstemmed	10.1007/s00521-018-3736-2 doi (DE-627)SPR040374831 (SPR)s00521-018-3736-2-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Peixoto, Solon Alves verfasserin aut Automatic classification of pulmonary diseases using a structural co-occurrence matrix 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision. Structural co-occurrence matrix (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Machine learning classifiers (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Lung disease (dpeaa)DE-He213 Filho, Pedro P. Rebouças verfasserin aut Arun Kumar, N. verfasserin aut de Albuquerque, Victor Hugo C. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2018), 15 vom: 20. Sept., Seite 10935-10945 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2018 number:15 day:20 month:09 pages:10935-10945 https://dx.doi.org/10.1007/s00521-018-3736-2 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_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_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 54.72 ASE AR 32 2018 15 20 09 10935-10945
allfieldsGer	10.1007/s00521-018-3736-2 doi (DE-627)SPR040374831 (SPR)s00521-018-3736-2-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Peixoto, Solon Alves verfasserin aut Automatic classification of pulmonary diseases using a structural co-occurrence matrix 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision. Structural co-occurrence matrix (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Machine learning classifiers (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Lung disease (dpeaa)DE-He213 Filho, Pedro P. Rebouças verfasserin aut Arun Kumar, N. verfasserin aut de Albuquerque, Victor Hugo C. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2018), 15 vom: 20. Sept., Seite 10935-10945 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2018 number:15 day:20 month:09 pages:10935-10945 https://dx.doi.org/10.1007/s00521-018-3736-2 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_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_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 54.72 ASE AR 32 2018 15 20 09 10935-10945
allfieldsSound	10.1007/s00521-018-3736-2 doi (DE-627)SPR040374831 (SPR)s00521-018-3736-2-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Peixoto, Solon Alves verfasserin aut Automatic classification of pulmonary diseases using a structural co-occurrence matrix 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision. Structural co-occurrence matrix (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Machine learning classifiers (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Lung disease (dpeaa)DE-He213 Filho, Pedro P. Rebouças verfasserin aut Arun Kumar, N. verfasserin aut de Albuquerque, Victor Hugo C. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 32(2018), 15 vom: 20. Sept., Seite 10935-10945 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:32 year:2018 number:15 day:20 month:09 pages:10935-10945 https://dx.doi.org/10.1007/s00521-018-3736-2 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_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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 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_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_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 54.72 ASE AR 32 2018 15 20 09 10935-10945
language	English
source	Enthalten in Neural computing & applications 32(2018), 15 vom: 20. Sept., Seite 10935-10945 volume:32 year:2018 number:15 day:20 month:09 pages:10935-10945
sourceStr	Enthalten in Neural computing & applications 32(2018), 15 vom: 20. Sept., Seite 10935-10945 volume:32 year:2018 number:15 day:20 month:09 pages:10935-10945
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Structural co-occurrence matrix Feature extraction Machine learning classifiers Medical image processing Lung disease
dewey-raw	004
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Peixoto, Solon Alves @@aut@@ Filho, Pedro P. Rebouças @@aut@@ Arun Kumar, N. @@aut@@ de Albuquerque, Victor Hugo C. @@aut@@
publishDateDaySort_date	2018-09-20T00:00:00Z
hierarchy_top_id	271595574
dewey-sort	14
id	SPR040374831
language_de	englisch
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author	Peixoto, Solon Alves
spellingShingle	Peixoto, Solon Alves ddc 004 bkl 54.72 misc Structural co-occurrence matrix misc Feature extraction misc Machine learning classifiers misc Medical image processing misc Lung disease Automatic classification of pulmonary diseases using a structural co-occurrence matrix
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topic_title	004 ASE 54.72 bkl Automatic classification of pulmonary diseases using a structural co-occurrence matrix Structural co-occurrence matrix (dpeaa)DE-He213 Feature extraction (dpeaa)DE-He213 Machine learning classifiers (dpeaa)DE-He213 Medical image processing (dpeaa)DE-He213 Lung disease (dpeaa)DE-He213
topic	ddc 004 bkl 54.72 misc Structural co-occurrence matrix misc Feature extraction misc Machine learning classifiers misc Medical image processing misc Lung disease
topic_unstemmed	ddc 004 bkl 54.72 misc Structural co-occurrence matrix misc Feature extraction misc Machine learning classifiers misc Medical image processing misc Lung disease
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title	Automatic classification of pulmonary diseases using a structural co-occurrence matrix
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title_full	Automatic classification of pulmonary diseases using a structural co-occurrence matrix
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title_sort	automatic classification of pulmonary diseases using a structural co-occurrence matrix
title_auth	Automatic classification of pulmonary diseases using a structural co-occurrence matrix
abstract	Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision.
abstractGer	Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision.
abstract_unstemmed	Abstract The World Health Organization (WHO) estimates that 300 million people have asthma and that this disease causes about 250 thousand deaths per year worldwide. The number of deaths from chronic obstructive pulmonary disease increased by 30% in 2015, and this disease will, according to WHO, be the third major cause of death worldwide in 2030. The identification of diseases using medical image processing techniques is in high demand to assist medical doctors to make more accurate diagnoses. However, although these techniques contribute in making medical diagnoses, most of them still need to have some parameters set and this can be a difficult and tedious process. In this paper, a new automatic approach to identify and classify lung diseases from a structural co-occurrence matrix (SCM) in chest computed tomography images is proposed. The most important novelty of this approach is that only the image is used as the input data and extract the structural information of the disease which, in this case, is related to the lower frequencies. In order to demonstrate the efficiency of the proposed technique, it was compared with other well-known state-of-art feature extractors. In addition, the SCM was evaluated with four filters (Gaussian, Fourier, Laplace and Sobel) using linear discriminant analysis, multi-layer perceptron, support vector machines and minimal learning machine classifiers. The results showed that the SCM, when using low frequencies, is able to adapt to different images and extract the most significant structural data, without the need of any additional parameters, yet maintaining the diagnostic precision.
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container_issue	15
title_short	Automatic classification of pulmonary diseases using a structural co-occurrence matrix
url	https://dx.doi.org/10.1007/s00521-018-3736-2
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author2	Filho, Pedro P. Rebouças Arun Kumar, N. de Albuquerque, Victor Hugo C.
author2Str	Filho, Pedro P. Rebouças Arun Kumar, N. de Albuquerque, Victor Hugo C.
ppnlink	271595574
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doi_str	10.1007/s00521-018-3736-2
up_date	2024-07-03T15:35:11.321Z
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Automatic classification of pulmonary diseases using a structural co-occurrence matrix

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