A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography
In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/10/1693 |
| _version_ | 1797600457026174976 |
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| author | Stefano Franceschini Maria Maddalena Autorino Michele Ambrosanio Vito Pascazio Fabio Baselice |
| author_facet | Stefano Franceschini Maria Maddalena Autorino Michele Ambrosanio Vito Pascazio Fabio Baselice |
| author_sort | Stefano Franceschini |
| collection | DOAJ |
| description | In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small. |
| first_indexed | 2024-03-11T03:48:22Z |
| format | Article |
| id | doaj.art-14d16ddb91e34e8088cc0b3ad3be761c |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T03:48:22Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-14d16ddb91e34e8088cc0b3ad3be761c2023-11-18T01:03:37ZengMDPI AGDiagnostics2075-44182023-05-011310169310.3390/diagnostics13101693A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave TomographyStefano Franceschini0Maria Maddalena Autorino1Michele Ambrosanio2Vito Pascazio3Fabio Baselice4Department of Engineering, University of Napoli Parthenope, Centro Direzionale, 80143 Napoli, ItalyDepartment of Engineering, University of Napoli Parthenope, Centro Direzionale, 80143 Napoli, ItalyDepartment of Economics, Law, Cybersecurity, and Sports Sciences, University of Napoli Parthenope, Via della Repubblica 32, 80035 Nola, ItalyDepartment of Engineering, University of Napoli Parthenope, Centro Direzionale, 80143 Napoli, ItalyDepartment of Engineering, University of Napoli Parthenope, Centro Direzionale, 80143 Napoli, ItalyIn this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small.https://www.mdpi.com/2075-4418/13/10/1693microwave tomographybreast cancer detectionneural networksbiomedical imagingartificial intelligenceelectromagnetic inverse scattering |
| spellingShingle | Stefano Franceschini Maria Maddalena Autorino Michele Ambrosanio Vito Pascazio Fabio Baselice A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography Diagnostics microwave tomography breast cancer detection neural networks biomedical imaging artificial intelligence electromagnetic inverse scattering |
| title | A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography |
| title_full | A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography |
| title_fullStr | A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography |
| title_full_unstemmed | A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography |
| title_short | A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography |
| title_sort | deep learning approach for diagnosis support in breast cancer microwave tomography |
| topic | microwave tomography breast cancer detection neural networks biomedical imaging artificial intelligence electromagnetic inverse scattering |
| url | https://www.mdpi.com/2075-4418/13/10/1693 |
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