Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model
Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing ra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/9/1/10 |
| _version_ | 1797617989422415872 |
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| author | Soumya Prakash Rana Maitreyee Dey Riccardo Loretoni Michele Duranti Mohammad Ghavami Sandra Dudley Gianluigi Tiberi |
| author_facet | Soumya Prakash Rana Maitreyee Dey Riccardo Loretoni Michele Duranti Mohammad Ghavami Sandra Dudley Gianluigi Tiberi |
| author_sort | Soumya Prakash Rana |
| collection | DOAJ |
| description | Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>21</mn></msub></semantics></math></inline-formula> signals in engineering terminology. Backscattered (complex) <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>21</mn></msub></semantics></math></inline-formula> signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model. |
| first_indexed | 2024-03-11T08:03:49Z |
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| id | doaj.art-6a5d25dd652f4459af85b36015cf2b98 |
| institution | Directory Open Access Journal |
| issn | 2379-1381 2379-139X |
| language | English |
| last_indexed | 2024-03-11T08:03:49Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj.art-6a5d25dd652f4459af85b36015cf2b982023-11-16T23:36:24ZengMDPI AGTomography2379-13812379-139X2023-01-019110512910.3390/tomography9010010Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning ModelSoumya Prakash Rana0Maitreyee Dey1Riccardo Loretoni2Michele Duranti3Mohammad Ghavami4Sandra Dudley5Gianluigi Tiberi6School of Engineering, London South Bank University, London SE1 0AA, UKSchool of Engineering, London South Bank University, London SE1 0AA, UKBreast Screening and Diagnostic Breast Cancer Unit, AUSL Umbria 2, 06034 Foligno, ItalyDepartment of Diagnostic Imaging, Perugia Hospital, 06156 Perugia, ItalySchool of Engineering, London South Bank University, London SE1 0AA, UKSchool of Engineering, London South Bank University, London SE1 0AA, UKSchool of Engineering, London South Bank University, London SE1 0AA, UKMammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>21</mn></msub></semantics></math></inline-formula> signals in engineering terminology. Backscattered (complex) <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>S</mi><mn>21</mn></msub></semantics></math></inline-formula> signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.https://www.mdpi.com/2379-139X/9/1/10radiation-free technologynon-invasive lesion detectionX-ray free breast screeningMammoWave’s dielectric breast responsesupervised machine learning |
| spellingShingle | Soumya Prakash Rana Maitreyee Dey Riccardo Loretoni Michele Duranti Mohammad Ghavami Sandra Dudley Gianluigi Tiberi Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model Tomography radiation-free technology non-invasive lesion detection X-ray free breast screening MammoWave’s dielectric breast response supervised machine learning |
| title | Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model |
| title_full | Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model |
| title_fullStr | Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model |
| title_full_unstemmed | Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model |
| title_short | Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model |
| title_sort | radiation free microwave technology for breast lesion detection using supervised machine learning model |
| topic | radiation-free technology non-invasive lesion detection X-ray free breast screening MammoWave’s dielectric breast response supervised machine learning |
| url | https://www.mdpi.com/2379-139X/9/1/10 |
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