Diagnosis of breast cancer based on modern mammography using hybrid transfer learning
Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modi...
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| Autor*in: |
Khamparia, Aditya [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Multidimensional systems and signal processing - Springer US, 1990, 32(2021), 2 vom: 11. Jan., Seite 747-765 |
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| Übergeordnetes Werk: |
volume:32 ; year:2021 ; number:2 ; day:11 ; month:01 ; pages:747-765 |
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| DOI / URN: |
10.1007/s11045-020-00756-7 |
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| 520 | |a Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. | ||
| 650 | 4 | |a Hybrid transfer learning | |
| 650 | 4 | |a Medical image segmentation | |
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Mammography | |
| 650 | 4 | |a 3D mammography | |
| 650 | 4 | |a Convolutional neural networks | |
| 700 | 1 | |a Bharati, Subrato |4 aut | |
| 700 | 1 | |a Podder, Prajoy |4 aut | |
| 700 | 1 | |a Gupta, Deepak |4 aut | |
| 700 | 1 | |a Khanna, Ashish |4 aut | |
| 700 | 1 | |a Phung, Thai Kim |4 aut | |
| 700 | 1 | |a Thanh, Dang N. H. |0 (orcid)0000-0003-2025-8319 |4 aut | |
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10.1007/s11045-020-00756-7 doi (DE-627)OLC2124426273 (DE-He213)s11045-020-00756-7-p DE-627 ger DE-627 rakwb eng 510 VZ Khamparia, Aditya verfasserin aut Diagnosis of breast cancer based on modern mammography using hybrid transfer learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. Hybrid transfer learning Medical image segmentation Breast cancer Mammography 3D mammography Convolutional neural networks Bharati, Subrato aut Podder, Prajoy aut Gupta, Deepak aut Khanna, Ashish aut Phung, Thai Kim aut Thanh, Dang N. H. (orcid)0000-0003-2025-8319 aut Enthalten in Multidimensional systems and signal processing Springer US, 1990 32(2021), 2 vom: 11. Jan., Seite 747-765 (DE-627)130892076 (DE-600)1041098-3 (DE-576)038686074 0923-6082 nnns volume:32 year:2021 number:2 day:11 month:01 pages:747-765 https://doi.org/10.1007/s11045-020-00756-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT AR 32 2021 2 11 01 747-765 |
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10.1007/s11045-020-00756-7 doi (DE-627)OLC2124426273 (DE-He213)s11045-020-00756-7-p DE-627 ger DE-627 rakwb eng 510 VZ Khamparia, Aditya verfasserin aut Diagnosis of breast cancer based on modern mammography using hybrid transfer learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. Hybrid transfer learning Medical image segmentation Breast cancer Mammography 3D mammography Convolutional neural networks Bharati, Subrato aut Podder, Prajoy aut Gupta, Deepak aut Khanna, Ashish aut Phung, Thai Kim aut Thanh, Dang N. H. (orcid)0000-0003-2025-8319 aut Enthalten in Multidimensional systems and signal processing Springer US, 1990 32(2021), 2 vom: 11. Jan., Seite 747-765 (DE-627)130892076 (DE-600)1041098-3 (DE-576)038686074 0923-6082 nnns volume:32 year:2021 number:2 day:11 month:01 pages:747-765 https://doi.org/10.1007/s11045-020-00756-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT AR 32 2021 2 11 01 747-765 |
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10.1007/s11045-020-00756-7 doi (DE-627)OLC2124426273 (DE-He213)s11045-020-00756-7-p DE-627 ger DE-627 rakwb eng 510 VZ Khamparia, Aditya verfasserin aut Diagnosis of breast cancer based on modern mammography using hybrid transfer learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. Hybrid transfer learning Medical image segmentation Breast cancer Mammography 3D mammography Convolutional neural networks Bharati, Subrato aut Podder, Prajoy aut Gupta, Deepak aut Khanna, Ashish aut Phung, Thai Kim aut Thanh, Dang N. H. (orcid)0000-0003-2025-8319 aut Enthalten in Multidimensional systems and signal processing Springer US, 1990 32(2021), 2 vom: 11. Jan., Seite 747-765 (DE-627)130892076 (DE-600)1041098-3 (DE-576)038686074 0923-6082 nnns volume:32 year:2021 number:2 day:11 month:01 pages:747-765 https://doi.org/10.1007/s11045-020-00756-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT AR 32 2021 2 11 01 747-765 |
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10.1007/s11045-020-00756-7 doi (DE-627)OLC2124426273 (DE-He213)s11045-020-00756-7-p DE-627 ger DE-627 rakwb eng 510 VZ Khamparia, Aditya verfasserin aut Diagnosis of breast cancer based on modern mammography using hybrid transfer learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. Hybrid transfer learning Medical image segmentation Breast cancer Mammography 3D mammography Convolutional neural networks Bharati, Subrato aut Podder, Prajoy aut Gupta, Deepak aut Khanna, Ashish aut Phung, Thai Kim aut Thanh, Dang N. H. (orcid)0000-0003-2025-8319 aut Enthalten in Multidimensional systems and signal processing Springer US, 1990 32(2021), 2 vom: 11. Jan., Seite 747-765 (DE-627)130892076 (DE-600)1041098-3 (DE-576)038686074 0923-6082 nnns volume:32 year:2021 number:2 day:11 month:01 pages:747-765 https://doi.org/10.1007/s11045-020-00756-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT AR 32 2021 2 11 01 747-765 |
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10.1007/s11045-020-00756-7 doi (DE-627)OLC2124426273 (DE-He213)s11045-020-00756-7-p DE-627 ger DE-627 rakwb eng 510 VZ Khamparia, Aditya verfasserin aut Diagnosis of breast cancer based on modern mammography using hybrid transfer learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. Hybrid transfer learning Medical image segmentation Breast cancer Mammography 3D mammography Convolutional neural networks Bharati, Subrato aut Podder, Prajoy aut Gupta, Deepak aut Khanna, Ashish aut Phung, Thai Kim aut Thanh, Dang N. H. (orcid)0000-0003-2025-8319 aut Enthalten in Multidimensional systems and signal processing Springer US, 1990 32(2021), 2 vom: 11. Jan., Seite 747-765 (DE-627)130892076 (DE-600)1041098-3 (DE-576)038686074 0923-6082 nnns volume:32 year:2021 number:2 day:11 month:01 pages:747-765 https://doi.org/10.1007/s11045-020-00756-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT AR 32 2021 2 11 01 747-765 |
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Diagnosis of breast cancer based on modern mammography using hybrid transfer learning |
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Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstractGer |
Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| title_short |
Diagnosis of breast cancer based on modern mammography using hybrid transfer learning |
| url |
https://doi.org/10.1007/s11045-020-00756-7 |
| remote_bool |
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| author2 |
Bharati, Subrato Podder, Prajoy Gupta, Deepak Khanna, Ashish Phung, Thai Kim Thanh, Dang N. H. |
| author2Str |
Bharati, Subrato Podder, Prajoy Gupta, Deepak Khanna, Ashish Phung, Thai Kim Thanh, Dang N. H. |
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130892076 |
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| doi_str |
10.1007/s11045-020-00756-7 |
| up_date |
2024-07-03T23:39:06.601Z |
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