A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis
Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fanizzi, Annarita [verfasserIn] Basile, Teresa M. A. [verfasserIn] Losurdo, Liliana [verfasserIn] Bellotti, Roberto [verfasserIn] Bottigli, Ubaldo [verfasserIn] Dentamaro, Rosalba [verfasserIn] Didonna, Vittorio [verfasserIn] Fausto, Alfonso [verfasserIn] Massafra, Raffaella [verfasserIn] Moschetta, Marco [verfasserIn] Popescu, Ondina [verfasserIn] Tamborra, Pasquale [verfasserIn] Tangaro, Sabina [verfasserIn] La Forgia, Daniele [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Übergeordnetes Werk: |
Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 21(2020), Suppl 2 vom: 11. März |
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| Übergeordnetes Werk: |
volume:21 ; year:2020 ; number:Suppl 2 ; day:11 ; month:03 |
| Links: |
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| DOI / URN: |
10.1186/s12859-020-3358-4 |
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SPR03908437X |
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| 245 | 1 | 2 | |a A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis |
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| 520 | |a Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. | ||
| 650 | 4 | |a Computer-aided diagnosis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Microcalcifications |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Digital mammograms |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Haar wavelet transform |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a SURF |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Minimum eigenvalue algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random forest |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feature selection |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Basile, Teresa M. A. |e verfasserin |4 aut | |
| 700 | 1 | |a Losurdo, Liliana |e verfasserin |4 aut | |
| 700 | 1 | |a Bellotti, Roberto |e verfasserin |4 aut | |
| 700 | 1 | |a Bottigli, Ubaldo |e verfasserin |4 aut | |
| 700 | 1 | |a Dentamaro, Rosalba |e verfasserin |4 aut | |
| 700 | 1 | |a Didonna, Vittorio |e verfasserin |4 aut | |
| 700 | 1 | |a Fausto, Alfonso |e verfasserin |4 aut | |
| 700 | 1 | |a Massafra, Raffaella |e verfasserin |4 aut | |
| 700 | 1 | |a Moschetta, Marco |e verfasserin |4 aut | |
| 700 | 1 | |a Popescu, Ondina |e verfasserin |4 aut | |
| 700 | 1 | |a Tamborra, Pasquale |e verfasserin |4 aut | |
| 700 | 1 | |a Tangaro, Sabina |e verfasserin |4 aut | |
| 700 | 1 | |a La Forgia, Daniele |e verfasserin |4 aut | |
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10.1186/s12859-020-3358-4 doi (DE-627)SPR03908437X (SPR)s12859-020-3358-4-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Fanizzi, Annarita verfasserin aut A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. Computer-aided diagnosis (dpeaa)DE-He213 Microcalcifications (dpeaa)DE-He213 Digital mammograms (dpeaa)DE-He213 Haar wavelet transform (dpeaa)DE-He213 SURF (dpeaa)DE-He213 Minimum eigenvalue algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Basile, Teresa M. A. verfasserin aut Losurdo, Liliana verfasserin aut Bellotti, Roberto verfasserin aut Bottigli, Ubaldo verfasserin aut Dentamaro, Rosalba verfasserin aut Didonna, Vittorio verfasserin aut Fausto, Alfonso verfasserin aut Massafra, Raffaella verfasserin aut Moschetta, Marco verfasserin aut Popescu, Ondina verfasserin aut Tamborra, Pasquale verfasserin aut Tangaro, Sabina verfasserin aut La Forgia, Daniele verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), Suppl 2 vom: 11. März (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:Suppl 2 day:11 month:03 https://dx.doi.org/10.1186/s12859-020-3358-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 Suppl 2 11 03 |
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10.1186/s12859-020-3358-4 doi (DE-627)SPR03908437X (SPR)s12859-020-3358-4-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Fanizzi, Annarita verfasserin aut A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. Computer-aided diagnosis (dpeaa)DE-He213 Microcalcifications (dpeaa)DE-He213 Digital mammograms (dpeaa)DE-He213 Haar wavelet transform (dpeaa)DE-He213 SURF (dpeaa)DE-He213 Minimum eigenvalue algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Basile, Teresa M. A. verfasserin aut Losurdo, Liliana verfasserin aut Bellotti, Roberto verfasserin aut Bottigli, Ubaldo verfasserin aut Dentamaro, Rosalba verfasserin aut Didonna, Vittorio verfasserin aut Fausto, Alfonso verfasserin aut Massafra, Raffaella verfasserin aut Moschetta, Marco verfasserin aut Popescu, Ondina verfasserin aut Tamborra, Pasquale verfasserin aut Tangaro, Sabina verfasserin aut La Forgia, Daniele verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), Suppl 2 vom: 11. März (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:Suppl 2 day:11 month:03 https://dx.doi.org/10.1186/s12859-020-3358-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 Suppl 2 11 03 |
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10.1186/s12859-020-3358-4 doi (DE-627)SPR03908437X (SPR)s12859-020-3358-4-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Fanizzi, Annarita verfasserin aut A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. Computer-aided diagnosis (dpeaa)DE-He213 Microcalcifications (dpeaa)DE-He213 Digital mammograms (dpeaa)DE-He213 Haar wavelet transform (dpeaa)DE-He213 SURF (dpeaa)DE-He213 Minimum eigenvalue algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Basile, Teresa M. A. verfasserin aut Losurdo, Liliana verfasserin aut Bellotti, Roberto verfasserin aut Bottigli, Ubaldo verfasserin aut Dentamaro, Rosalba verfasserin aut Didonna, Vittorio verfasserin aut Fausto, Alfonso verfasserin aut Massafra, Raffaella verfasserin aut Moschetta, Marco verfasserin aut Popescu, Ondina verfasserin aut Tamborra, Pasquale verfasserin aut Tangaro, Sabina verfasserin aut La Forgia, Daniele verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), Suppl 2 vom: 11. März (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:Suppl 2 day:11 month:03 https://dx.doi.org/10.1186/s12859-020-3358-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 Suppl 2 11 03 |
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10.1186/s12859-020-3358-4 doi (DE-627)SPR03908437X (SPR)s12859-020-3358-4-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Fanizzi, Annarita verfasserin aut A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. Computer-aided diagnosis (dpeaa)DE-He213 Microcalcifications (dpeaa)DE-He213 Digital mammograms (dpeaa)DE-He213 Haar wavelet transform (dpeaa)DE-He213 SURF (dpeaa)DE-He213 Minimum eigenvalue algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Basile, Teresa M. A. verfasserin aut Losurdo, Liliana verfasserin aut Bellotti, Roberto verfasserin aut Bottigli, Ubaldo verfasserin aut Dentamaro, Rosalba verfasserin aut Didonna, Vittorio verfasserin aut Fausto, Alfonso verfasserin aut Massafra, Raffaella verfasserin aut Moschetta, Marco verfasserin aut Popescu, Ondina verfasserin aut Tamborra, Pasquale verfasserin aut Tangaro, Sabina verfasserin aut La Forgia, Daniele verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), Suppl 2 vom: 11. März (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:Suppl 2 day:11 month:03 https://dx.doi.org/10.1186/s12859-020-3358-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 Suppl 2 11 03 |
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10.1186/s12859-020-3358-4 doi (DE-627)SPR03908437X (SPR)s12859-020-3358-4-e DE-627 ger DE-627 rakwb eng 004 570 610 ASE 42.11 bkl 54.00 bkl Fanizzi, Annarita verfasserin aut A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. Computer-aided diagnosis (dpeaa)DE-He213 Microcalcifications (dpeaa)DE-He213 Digital mammograms (dpeaa)DE-He213 Haar wavelet transform (dpeaa)DE-He213 SURF (dpeaa)DE-He213 Minimum eigenvalue algorithm (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Basile, Teresa M. A. verfasserin aut Losurdo, Liliana verfasserin aut Bellotti, Roberto verfasserin aut Bottigli, Ubaldo verfasserin aut Dentamaro, Rosalba verfasserin aut Didonna, Vittorio verfasserin aut Fausto, Alfonso verfasserin aut Massafra, Raffaella verfasserin aut Moschetta, Marco verfasserin aut Popescu, Ondina verfasserin aut Tamborra, Pasquale verfasserin aut Tangaro, Sabina verfasserin aut La Forgia, Daniele verfasserin aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 21(2020), Suppl 2 vom: 11. März (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:21 year:2020 number:Suppl 2 day:11 month:03 https://dx.doi.org/10.1186/s12859-020-3358-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 42.11 ASE 54.00 ASE AR 21 2020 Suppl 2 11 03 |
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Enthalten in BMC bioinformatics 21(2020), Suppl 2 vom: 11. März volume:21 year:2020 number:Suppl 2 day:11 month:03 |
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Fanizzi, Annarita @@aut@@ Basile, Teresa M. A. @@aut@@ Losurdo, Liliana @@aut@@ Bellotti, Roberto @@aut@@ Bottigli, Ubaldo @@aut@@ Dentamaro, Rosalba @@aut@@ Didonna, Vittorio @@aut@@ Fausto, Alfonso @@aut@@ Massafra, Raffaella @@aut@@ Moschetta, Marco @@aut@@ Popescu, Ondina @@aut@@ Tamborra, Pasquale @@aut@@ Tangaro, Sabina @@aut@@ La Forgia, Daniele @@aut@@ |
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Fanizzi, Annarita Basile, Teresa M. A. Losurdo, Liliana Bellotti, Roberto Bottigli, Ubaldo Dentamaro, Rosalba Didonna, Vittorio Fausto, Alfonso Massafra, Raffaella Moschetta, Marco Popescu, Ondina Tamborra, Pasquale Tangaro, Sabina La Forgia, Daniele |
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machine learning approach on multiscale texture analysis for breast microcalcification diagnosis |
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A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis |
| abstract |
Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. |
| abstractGer |
Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. |
| abstract_unstemmed |
Background Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. Results For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters. |
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Basile, Teresa M. A. Losurdo, Liliana Bellotti, Roberto Bottigli, Ubaldo Dentamaro, Rosalba Didonna, Vittorio Fausto, Alfonso Massafra, Raffaella Moschetta, Marco Popescu, Ondina Tamborra, Pasquale Tangaro, Sabina La Forgia, Daniele |
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The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. Conclusions The best performing result obtained with embedded method is more parsimonious than the filter one. 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7.401613 |