Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

Este trabajo desarrolla un vector de soporte y una clasificación basada en los nervios de las regiones mamográficas mediante la aplicación de parámetros estadísticos de energía de paquetes de ondas y entropía de Tsallis. De los primeros cuatro niveles de descomposición de paquetes de ondículas, se e...

Full description

Autores:: Ramírez Moreno, David Fernando
Ramirez Villegas, Juan Felipe

Tipo de recurso:: Article of journal

Fecha de publicación:: 2012

Institución:: Universidad Autónoma de Occidente

Repositorio:: RED: Repositorio Educativo Digital UAO

Idioma:: eng

id	REPOUAO2_32cffabb820a40122fc91d21085414e1
oai_identifier_str	oai:red.uao.edu.co:10614/11975
network_acronym_str	REPOUAO2
network_name_str	RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
title	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
spellingShingle	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions Mammographic regions Wavelet packet Wavelet energy Tsallis entropy Correlation analysis Logistic regression Sequential forward selection Support vector machine Multi-layer perceptron
title_short	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
title_full	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
title_fullStr	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
title_full_unstemmed	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
title_sort	Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions
dc.creator.fl_str_mv	Ramírez Moreno, David Fernando Ramirez Villegas, Juan Felipe
dc.contributor.author.none.fl_str_mv	Ramírez Moreno, David Fernando Ramirez Villegas, Juan Felipe
dc.subject.proposal.eng.fl_str_mv	Mammographic regions Wavelet packet Wavelet energy Tsallis entropy Correlation analysis Logistic regression Sequential forward selection Support vector machine Multi-layer perceptron
topic	Mammographic regions Wavelet packet Wavelet energy Tsallis entropy Correlation analysis Logistic regression Sequential forward selection Support vector machine Multi-layer perceptron
description	Este trabajo desarrolla un vector de soporte y una clasificación basada en los nervios de las regiones mamográficas mediante la aplicación de parámetros estadísticos de energía de paquetes de ondas y entropía de Tsallis. De los primeros cuatro niveles de descomposición de paquetes de ondículas, se evaluaron cuatro conjuntos de características diferentes utilizando la prueba de Kolmogorov-Smirnov de dos muestras (prueba KS) y, en un caso, el análisis de componentes principales (PCA). La selección de características se realizó aplicando un esquema híbrido que integra una prueba KS no paramétrica, un análisis de correlación, un modelo de regresión logística (LR) y una selección directa secuencial (SFS). Las características principales seleccionadas (según el nivel de descomposición de ondículas seleccionado) produjeron los mejores rendimientos de clasificación en comparación con otros métodos de selección de características bien conocidos. La clasificación de los datos se llevó a cabo utilizando varios esquemas de máquina de vectores de soporte (SVM) y redes neuronales de perceptrón multicapa (MLP). El nuevo conjunto de características mejoró significativamente el rendimiento de clasificación de las regiones mamográficas utilizando SVM y MLP convencionales
publishDate	2012
dc.date.issued.none.fl_str_mv	2012-02
dc.date.accessioned.none.fl_str_mv	2020-02-21T14:14:14Z
dc.date.available.none.fl_str_mv	2020-02-21T14:14:14Z
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.eng.fl_str_mv	http://purl.org/coar/resource_type/c_6501
dc.type.content.eng.fl_str_mv	Text
dc.type.driver.eng.fl_str_mv	info:eu-repo/semantics/article
dc.type.redcol.eng.fl_str_mv	http://purl.org/redcol/resource_type/ARTREF
dc.type.version.eng.fl_str_mv	info:eu-repo/semantics/publishedVersion
format	http://purl.org/coar/resource_type/c_6501
status_str	publishedVersion
dc.identifier.uri.none.fl_str_mv	http://red.uao.edu.co//handle/10614/11975
dc.identifier.doi.eng.fl_str_mv	https://doi.org/10.1016/j.neucom.2011.08.015
url	http://red.uao.edu.co//handle/10614/11975 https://doi.org/10.1016/j.neucom.2011.08.015
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.relation.eng.fl_str_mv	Neurocomputing. Volumen 77, número 1, (febrero 2012); páginas 82-100
dc.relation.citationissue.none.fl_str_mv	1
dc.relation.citationvolume.none.fl_str_mv	77
dc.relation.cites.spa.fl_str_mv	Ramirez-Villegas, J. F., Ramirez-Moreno D. F (2012). Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. Neurocomputing. 77(1) , 82-100 .https://doi.org/10.1016/j.neucom.2011.08.015.
dc.relation.ispartofjournal.eng.fl_str_mv	Neurocomputing
dc.relation.references.none.fl_str_mv	B. VermaNovel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms Artif. Intell. Med., 42 (2008), pp. 67-79 A. Vilarrasa-Andrés, Sistema inteligente para la detección y diagnóstico de patología mamaria, Ph.D. Thesis, Dept. de radiología y medicina física, Universidad Complutense de Madrid, Madrid, España, 2006. J.F. Ramirez-Villegas, E. Lam-Espinosa, D.F. Ramirez-Moreno, Microcalcification detection in mammograms using difference of Gaussians filters and a hybrid feedforward-Kohonen neural network, in: XXII Brazilian Symposium on Computer Graphics and Image Processing (2009) 186–193, ISSN: 1550–1834, doi:. K. DoiComputer aided diagnosis in medical imaging: historical review, current status and future potential Computer. Med. Imaging Graphics, 31 (2007), pp. 198-211 R.M. Rangayyana, F.J. Ayresa, J.E.Leo DesautelsA review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs J. Franklin Inst., 344 (2007), p. 312 348 H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, X.N. Du. Approaches for automated detection and classification of masses in mammograms Pattern Recognition, 39 (2006), pp. 646-668 N.R. Pal, B. Bhowmick, S.K. Patel, S. Pal, J. DasA multi-stage neural network aided system for detection of microcalcifications in digitized mammograms Neurocomputing, 71 (2008), pp. 2625-2634 L. Bocchi, G. Coppini, J. Nori, G. ValliDetection of single and clustered microcalcifications in mammograms using fractals models and neural networks Med. Eng. Phys., 26 (2004), pp. 303-312 B. Verma, P. McLeod, A. KlevanskyA novel soft cluster neural network for the classification of suspicious areas in digital mammograms Pattern Recognition, 42 (2009), pp. 1845-1852 C. Balleyguier, K. Kinkel, J. Fermanian, S. Malan, G. Djen, P. Taourel, O. HelenonComputer-aided detection (CAD) in mammography: does it help the junior or the senior radiologist? Eur. J. Radiol., 54 (2005), pp. 90-96 Y. Lee, S. Tsai, Computerized classification of microcalcifications on mammograms using fuzzy logic and genetic algorithm, in: J. Fitzpatrick, M. Sonka (Eds.), Proceedings of the SPIE in Medical Imaging 2004: Image Processing, 2004, pp. 952–959 N. Ramirez, H. Acosta-Mesa, H. Carillo-Calvert, L. Nava-Fernandez, R. Barrientos-MartinezDiagnosis of breast cancer using Bayesian Networks: a case study Comput. Biol. Med., 37 (2007), pp. 1553-1564 L. Wei, Y. Yang, R.M. NishikawaMicrocalcification classification assisted by content-based image retrieval for breast cancer diagnosis Pattern Recognition, 42 (2009), pp. 1126-1132 D. Wang, L. Shi, P.A. HengAutomatic detection of breast cancers in mammograms using structured support vector machines Neurocomputing, 72 (2009), pp. 3296-3302 H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, H.N. DuApproaches for automated detection and classification of masses in mammograms Pattern Recognition, 39 (2006), pp. 646-668 L. Shen, R.M. Rangayyan, J.E.L. DesautelsDetection and classification of mammographic calcifications Int. J. Pattern Recognition Artif. Intell., 7 (1993), pp. 1403-1416 I. Christoyianni, A. Koutras, E. Dermatas, G. KokkinakisComputer aided diagnosis of breast cancer in digitized mammograms Comput. Med. Imaging Graphics, 26 (2002), pp. 309-319 I. El-Naqa, Y. Yang, M.N. Wernick, N.P. Galatsanos, R.M. NishikawaA support vector machine approach for detection of microcalcifications IEEE Trans. Med. Imaging, 21 (2002), pp. 1552-1563 S. Yu, L. GuanA CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films IEEE Trans. Med. Imaging, 19 (2000), pp. 115-126 J. SucklingThe mammographic image analysis society digital mammogram database Exerpta Med. Int. Cong. Ser., 1069 (1994), pp. 375-378 A. Papadopoulos, D.I. Fotiadis, L. CostaridouImprovement of microcalcifications cluster detection in mammography utilizing image enhancing techniques Comput. Biol. Med., 38 (2008), pp. 1045-1055 S. MallatA theory for multiresolution signal decomposition: the wavelet representation IEEE Pattern Anal. Mach. Intell., 11 (1989), pp. 674-693 R.R. Coifman, M.V. WickerhauserExperiments with adapted wavelet de-noising for medical signals and images M. Akay (Ed.), Time Frequency and Wavelets in Biomedical Signal Processing (1998) S. Bouyahia, J. Mbainaibeye, N. EllouzeWavelet based microcalcifications detection in digitized mammograms ICGST-GVIP J., 8 (2009), pp. 23-31 C.H. Chen, G.G. LeeOn digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis Graphical Models Image Process., 59 (1997), pp. 349-364 S. Bouyahia, J. Mbainaibeye, N. Ellouze, Wavelets and wavelet packets for mammography, in: 3rd International Conference: Sciences of Electronic Technologies of Information and Telecommunications (SETIT), 2005 R. Coifman, Y. Meyer, S. Quake, V. Wicker-hauserSignal Processing and Compression with Wavelet Packets Yale University, New Haven, CT (1990) J. Mohanalin, P.K. Kalra, N. KumarTsallis entropy based microcalcification segmentation ICGST-GVIP J., 9 (2009), pp. 49-55 M. Portes de AlbuquerqueImage thresholding using Tsallis entropy Pattern Recognition Lett., 25 (2004), pp. 1059-1065 R.R. Coifman, M.V. WickerhauserEntropy based algorithms for best basis selection IEEE Trans. Inf. Theory, 32 (1992), pp. 712-718 M.V. WickerhauserAdapted Wavelet Analysis from Theory to Software AK Peters, Ltd., Wellsley, MA (1994) A.C. Rencher, Methods of Multivariate Analysis, Wiley Series in Probability and Statistics, 2002 S. Theodoridis, K. KoutroumbasPattern Recognition Academic Press, New York (2003) P. Komarek, Logistic regression for data mining and high-dimensional classification, Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 2004. H. Georgiou, M. Mavroforakis, D. Dimitropoulos, Theodoridis S. CavourasMulti-scaled morphological features for the characterization of mammographic masses using statistical classification schemes Artif. Intell. Med., 41 (2007), pp. 39-55 S. HaykinNeural Networks: A Comprehensive Foundation Prentice-Hall, Inc. (1999) J.A. Swets, R.M. PickettEvaluation of Diagnostic Systems: Methods from Signal Detection Theory Academic Press, New York (1982) T. FawcettAn introduction to ROC analysis Patern Recognition Lett., 27 (2006), pp. 861-874 S. Agarwal, T. Graepel, S. Har-Peled, R. Herbrich, D. RothGeneralization bounds for the area under the ROC curve J. Mach. Learn. Res., 6 (2005), pp. 393-425 M. Mavroforakis, H. Georgiou, N. Dimitropoulos, D. Cavouras, S. TheodoridisSignificance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines Eur. J. Radiol., 54 (2005), pp. 80-89 C. Marrocco, M. Molinara, C. D'Elia, F. TortorellaA computer-aided detection system for clustered microcalcifications Artif. Intell. Med., 50 (2010), pp. 23-32 N.-C. Tsai, H.-W. Chen, S.-L. HsuComputer-aided diagnosis for early-stage breast cancer by using Wavelet Transform Comput. Med. Imaging Graphics, 35 (2011), pp. 1-8
dc.rights.spa.fl_str_mv	Derechos Reservados - Universidad Autónoma de Occidente
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.uri.eng.fl_str_mv	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.eng.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.creativecommons.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
rights_invalid_str_mv	Derechos Reservados - Universidad Autónoma de Occidente https://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.eng.fl_str_mv	application/pdf
dc.format.extent.spa.fl_str_mv	19 páginas
dc.coverage.spatial.none.fl_str_mv	Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí
dc.publisher.eng.fl_str_mv	Elsevier
institution	Universidad Autónoma de Occidente
bitstream.url.fl_str_mv	https://red.uao.edu.co/bitstreams/9845264f-54e7-4303-91f1-9be8b9cb63e1/download https://red.uao.edu.co/bitstreams/7e82eb15-1753-4381-828b-8e0d352496af/download
bitstream.checksum.fl_str_mv	4460e5956bc1d1639be9ae6146a50347 20b5ba22b1117f71589c7318baa2c560
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5
repository.name.fl_str_mv	Repositorio Digital Universidad Autonoma de Occidente
repository.mail.fl_str_mv	repositorio@uao.edu.co
_version_	1808479035386232832
spelling	Ramírez Moreno, David Fernandovirtual::4342-1Ramirez Villegas, Juan Felipede4a5d2f855a047341c6903be500d787Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2020-02-21T14:14:14Z2020-02-21T14:14:14Z2012-02http://red.uao.edu.co//handle/10614/11975https://doi.org/10.1016/j.neucom.2011.08.015Este trabajo desarrolla un vector de soporte y una clasificación basada en los nervios de las regiones mamográficas mediante la aplicación de parámetros estadísticos de energía de paquetes de ondas y entropía de Tsallis. De los primeros cuatro niveles de descomposición de paquetes de ondículas, se evaluaron cuatro conjuntos de características diferentes utilizando la prueba de Kolmogorov-Smirnov de dos muestras (prueba KS) y, en un caso, el análisis de componentes principales (PCA). La selección de características se realizó aplicando un esquema híbrido que integra una prueba KS no paramétrica, un análisis de correlación, un modelo de regresión logística (LR) y una selección directa secuencial (SFS). Las características principales seleccionadas (según el nivel de descomposición de ondículas seleccionado) produjeron los mejores rendimientos de clasificación en comparación con otros métodos de selección de características bien conocidos. La clasificación de los datos se llevó a cabo utilizando varios esquemas de máquina de vectores de soporte (SVM) y redes neuronales de perceptrón multicapa (MLP). El nuevo conjunto de características mejoró significativamente el rendimiento de clasificación de las regiones mamográficas utilizando SVM y MLP convencionalesThis work develops a support vector and neural-based classification of mammographic regions by applying statistical, wavelet packet energy and Tsallis entropy parameterization. From the first four wavelet packet decomposition levels, four different feature sets were evaluated using two-sample Kolmogorov–Smirnov test (KS-test) and, in one case, principal component analysis (PCA). Feature selection was performed applying a hybrid scheme integrating non-parametric KS-test, correlation analysis, a logistic regression (LR) model and sequential forward selection (SFS). The top selected features (depending on the selected wavelet decomposition level) produced the best classification performances in comparison to other well-known feature selection methods. The classification of the data was carried out using several support vector machine (SVM) schemes and multi-layer perceptron (MLP) neural networks. The new set of features improved significantly the classification performance of mammographic regions using conventional SVMs and MLPs.application/pdf19 páginasengElsevierNeurocomputing. Volumen 77, número 1, (febrero 2012); páginas 82-100177Ramirez-Villegas, J. F., Ramirez-Moreno D. F (2012). Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions. Neurocomputing. 77(1) , 82-100 .https://doi.org/10.1016/j.neucom.2011.08.015.NeurocomputingB. VermaNovel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms Artif. Intell. Med., 42 (2008), pp. 67-79A. Vilarrasa-Andrés, Sistema inteligente para la detección y diagnóstico de patología mamaria, Ph.D. Thesis, Dept. de radiología y medicina física, Universidad Complutense de Madrid, Madrid, España, 2006.J.F. Ramirez-Villegas, E. Lam-Espinosa, D.F. Ramirez-Moreno, Microcalcification detection in mammograms using difference of Gaussians filters and a hybrid feedforward-Kohonen neural network, in: XXII Brazilian Symposium on Computer Graphics and Image Processing (2009) 186–193, ISSN: 1550–1834, doi:.K. DoiComputer aided diagnosis in medical imaging: historical review, current status and future potential Computer. Med. Imaging Graphics, 31 (2007), pp. 198-211R.M. Rangayyana, F.J. Ayresa, J.E.Leo DesautelsA review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs J. Franklin Inst., 344 (2007), p. 312 348H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, X.N. Du. Approaches for automated detection and classification of masses in mammograms Pattern Recognition, 39 (2006), pp. 646-668N.R. Pal, B. Bhowmick, S.K. Patel, S. Pal, J. DasA multi-stage neural network aided system for detection of microcalcifications in digitized mammograms Neurocomputing, 71 (2008), pp. 2625-2634L. Bocchi, G. Coppini, J. Nori, G. ValliDetection of single and clustered microcalcifications in mammograms using fractals models and neural networks Med. Eng. Phys., 26 (2004), pp. 303-312B. Verma, P. McLeod, A. KlevanskyA novel soft cluster neural network for the classification of suspicious areas in digital mammograms Pattern Recognition, 42 (2009), pp. 1845-1852C. Balleyguier, K. Kinkel, J. Fermanian, S. Malan, G. Djen, P. Taourel, O. HelenonComputer-aided detection (CAD) in mammography: does it help the junior or the senior radiologist? Eur. J. Radiol., 54 (2005), pp. 90-96Y. Lee, S. Tsai, Computerized classification of microcalcifications on mammograms using fuzzy logic and genetic algorithm, in: J. Fitzpatrick, M. Sonka (Eds.), Proceedings of the SPIE in Medical Imaging 2004: Image Processing, 2004, pp. 952–959N. Ramirez, H. Acosta-Mesa, H. Carillo-Calvert, L. Nava-Fernandez, R. Barrientos-MartinezDiagnosis of breast cancer using Bayesian Networks: a case study Comput. Biol. Med., 37 (2007), pp. 1553-1564L. Wei, Y. Yang, R.M. NishikawaMicrocalcification classification assisted by content-based image retrieval for breast cancer diagnosis Pattern Recognition, 42 (2009), pp. 1126-1132D. Wang, L. Shi, P.A. HengAutomatic detection of breast cancers in mammograms using structured support vector machines Neurocomputing, 72 (2009), pp. 3296-3302H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, H.N. DuApproaches for automated detection and classification of masses in mammograms Pattern Recognition, 39 (2006), pp. 646-668L. Shen, R.M. Rangayyan, J.E.L. DesautelsDetection and classification of mammographic calcifications Int. J. Pattern Recognition Artif. Intell., 7 (1993), pp. 1403-1416I. Christoyianni, A. Koutras, E. Dermatas, G. KokkinakisComputer aided diagnosis of breast cancer in digitized mammograms Comput. Med. Imaging Graphics, 26 (2002), pp. 309-319I. El-Naqa, Y. Yang, M.N. Wernick, N.P. Galatsanos, R.M. NishikawaA support vector machine approach for detection of microcalcifications IEEE Trans. Med. Imaging, 21 (2002), pp. 1552-1563S. Yu, L. GuanA CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films IEEE Trans. Med. Imaging, 19 (2000), pp. 115-126J. SucklingThe mammographic image analysis society digital mammogram database Exerpta Med. Int. Cong. Ser., 1069 (1994), pp. 375-378A. Papadopoulos, D.I. Fotiadis, L. CostaridouImprovement of microcalcifications cluster detection in mammography utilizing image enhancing techniques Comput. Biol. Med., 38 (2008), pp. 1045-1055S. MallatA theory for multiresolution signal decomposition: the wavelet representation IEEE Pattern Anal. Mach. Intell., 11 (1989), pp. 674-693R.R. Coifman, M.V. WickerhauserExperiments with adapted wavelet de-noising for medical signals and images M. Akay (Ed.), Time Frequency and Wavelets in Biomedical Signal Processing (1998)S. Bouyahia, J. Mbainaibeye, N. EllouzeWavelet based microcalcifications detection in digitized mammograms ICGST-GVIP J., 8 (2009), pp. 23-31C.H. Chen, G.G. LeeOn digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis Graphical Models Image Process., 59 (1997), pp. 349-364S. Bouyahia, J. Mbainaibeye, N. Ellouze, Wavelets and wavelet packets for mammography, in: 3rd International Conference: Sciences of Electronic Technologies of Information and Telecommunications (SETIT), 2005R. Coifman, Y. Meyer, S. Quake, V. Wicker-hauserSignal Processing and Compression with Wavelet Packets Yale University, New Haven, CT (1990)J. Mohanalin, P.K. Kalra, N. KumarTsallis entropy based microcalcification segmentation ICGST-GVIP J., 9 (2009), pp. 49-55M. Portes de AlbuquerqueImage thresholding using Tsallis entropy Pattern Recognition Lett., 25 (2004), pp. 1059-1065R.R. Coifman, M.V. WickerhauserEntropy based algorithms for best basis selection IEEE Trans. Inf. Theory, 32 (1992), pp. 712-718M.V. WickerhauserAdapted Wavelet Analysis from Theory to Software AK Peters, Ltd., Wellsley, MA (1994)A.C. Rencher, Methods of Multivariate Analysis, Wiley Series in Probability and Statistics, 2002S. Theodoridis, K. KoutroumbasPattern Recognition Academic Press, New York (2003)P. Komarek, Logistic regression for data mining and high-dimensional classification, Ph.D. Thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 2004.H. Georgiou, M. Mavroforakis, D. Dimitropoulos, Theodoridis S. CavourasMulti-scaled morphological features for the characterization of mammographic masses using statistical classification schemes Artif. Intell. Med., 41 (2007), pp. 39-55S. HaykinNeural Networks: A Comprehensive Foundation Prentice-Hall, Inc. (1999)J.A. Swets, R.M. PickettEvaluation of Diagnostic Systems: Methods from Signal Detection Theory Academic Press, New York (1982)T. FawcettAn introduction to ROC analysis Patern Recognition Lett., 27 (2006), pp. 861-874S. Agarwal, T. Graepel, S. Har-Peled, R. Herbrich, D. RothGeneralization bounds for the area under the ROC curve J. Mach. Learn. Res., 6 (2005), pp. 393-425M. Mavroforakis, H. Georgiou, N. Dimitropoulos, D. Cavouras, S. TheodoridisSignificance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines Eur. J. Radiol., 54 (2005), pp. 80-89C. Marrocco, M. Molinara, C. D'Elia, F. TortorellaA computer-aided detection system for clustered microcalcifications Artif. Intell. Med., 50 (2010), pp. 23-32N.-C. Tsai, H.-W. Chen, S.-L. HsuComputer-aided diagnosis for early-stage breast cancer by using Wavelet Transform Comput. Med. Imaging Graphics, 35 (2011), pp. 1-8Derechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regionsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Mammographic regionsWavelet packetWavelet energyTsallis entropyCorrelation analysisLogistic regressionSequential forward selectionSupport vector machineMulti-layer perceptronPublication61e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::4342-161e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::4342-1https://scholar.google.com/citations?user=RTce1fkAAAAJ&hl=esvirtual::4342-10000-0003-2372-3554virtual::4342-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000353744virtual::4342-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://red.uao.edu.co/bitstreams/9845264f-54e7-4303-91f1-9be8b9cb63e1/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/7e82eb15-1753-4381-828b-8e0d352496af/download20b5ba22b1117f71589c7318baa2c560MD5310614/11975oai:red.uao.edu.co:10614/119752024-03-13 16:58:33.193https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidentemetadata.onlyhttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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

Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

Publicaciones similares