Brain MRI tumour classification using quantum classical convolutional neural net architecture
Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine...
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| Autor*in: |
Choudhuri, Rudrajit [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 35(2022), 6 vom: 22. Okt., Seite 4467-4478 |
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| Übergeordnetes Werk: |
volume:35 ; year:2022 ; number:6 ; day:22 ; month:10 ; pages:4467-4478 |
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| DOI / URN: |
10.1007/s00521-022-07939-2 |
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OLC2133634959 |
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| 520 | |a Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. | ||
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10.1007/s00521-022-07939-2 doi (DE-627)OLC2133634959 (DE-He213)s00521-022-07939-2-p DE-627 ger DE-627 rakwb eng 004 VZ Choudhuri, Rudrajit verfasserin aut Brain MRI tumour classification using quantum classical convolutional neural net architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. Hybrid quantum algorithm Quantum net Brain tumour classification Convolutional neural net Halder, Amiya (orcid)0000-0001-6733-8135 aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 6 vom: 22. Okt., Seite 4467-4478 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:6 day:22 month:10 pages:4467-4478 https://doi.org/10.1007/s00521-022-07939-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 6 22 10 4467-4478 |
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10.1007/s00521-022-07939-2 doi (DE-627)OLC2133634959 (DE-He213)s00521-022-07939-2-p DE-627 ger DE-627 rakwb eng 004 VZ Choudhuri, Rudrajit verfasserin aut Brain MRI tumour classification using quantum classical convolutional neural net architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. Hybrid quantum algorithm Quantum net Brain tumour classification Convolutional neural net Halder, Amiya (orcid)0000-0001-6733-8135 aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 6 vom: 22. Okt., Seite 4467-4478 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:6 day:22 month:10 pages:4467-4478 https://doi.org/10.1007/s00521-022-07939-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 6 22 10 4467-4478 |
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10.1007/s00521-022-07939-2 doi (DE-627)OLC2133634959 (DE-He213)s00521-022-07939-2-p DE-627 ger DE-627 rakwb eng 004 VZ Choudhuri, Rudrajit verfasserin aut Brain MRI tumour classification using quantum classical convolutional neural net architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. Hybrid quantum algorithm Quantum net Brain tumour classification Convolutional neural net Halder, Amiya (orcid)0000-0001-6733-8135 aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 6 vom: 22. Okt., Seite 4467-4478 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:6 day:22 month:10 pages:4467-4478 https://doi.org/10.1007/s00521-022-07939-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 6 22 10 4467-4478 |
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10.1007/s00521-022-07939-2 doi (DE-627)OLC2133634959 (DE-He213)s00521-022-07939-2-p DE-627 ger DE-627 rakwb eng 004 VZ Choudhuri, Rudrajit verfasserin aut Brain MRI tumour classification using quantum classical convolutional neural net architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. Hybrid quantum algorithm Quantum net Brain tumour classification Convolutional neural net Halder, Amiya (orcid)0000-0001-6733-8135 aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 6 vom: 22. Okt., Seite 4467-4478 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:6 day:22 month:10 pages:4467-4478 https://doi.org/10.1007/s00521-022-07939-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 6 22 10 4467-4478 |
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10.1007/s00521-022-07939-2 doi (DE-627)OLC2133634959 (DE-He213)s00521-022-07939-2-p DE-627 ger DE-627 rakwb eng 004 VZ Choudhuri, Rudrajit verfasserin aut Brain MRI tumour classification using quantum classical convolutional neural net architecture 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. Hybrid quantum algorithm Quantum net Brain tumour classification Convolutional neural net Halder, Amiya (orcid)0000-0001-6733-8135 aut Enthalten in Neural computing & applications Springer London, 1993 35(2022), 6 vom: 22. Okt., Seite 4467-4478 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:35 year:2022 number:6 day:22 month:10 pages:4467-4478 https://doi.org/10.1007/s00521-022-07939-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 35 2022 6 22 10 4467-4478 |
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Brain MRI tumour classification using quantum classical convolutional neural net architecture |
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brain mri tumour classification using quantum classical convolutional neural net architecture |
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Brain MRI tumour classification using quantum classical convolutional neural net architecture |
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Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The use of quantum machines leverages the performance of classical machines in many aspects of solving real-world problems. Classification of a brain MR image for detection of tumour regions is a widely performed diagnostic step when it comes to working with brain images. Classical machine learning methods and/or conventional deep learning architectures including convolutional nets are commonly used for the classification of images. With larger network size, training the model becomes a challenging task to undertake. Quantum algorithms are beneficial in optimizing the performance of classical algorithms by incorporating intrinsic properties of quantum bits. In this paper, a novel Quantum Classical ConvNet architecture (QCCNN) is proposed for a binary class classification of brain MR images for detection of tumour regions in the human brain. The underlying idea is to encode data into quantum states enabling a faster extraction of information followed by using the information to distinguish the class of data. The reliability and the robustness of the proposed architecture are highlighted by the results obtained by the classifier. With technological advancements in quantum computers in the future (more qubits and less noise), the performance of the approach can be further improved. The presented model is tested on various datasets (Brats 2013, Harvard Med School, private dataset) and on quantitative evaluation with standard metrics, and the robustness of the classifier is verified. The proposed QCCNN model achieves accuracies in the range of 97.5–98.72% on different datasets which defend the ability of the presented architecture in detecting and classifying brain tumours. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Brain MRI tumour classification using quantum classical convolutional neural net architecture |
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