Microwave-based Breast Cancer Detection by using Auto Encoder-Decoder Probabilistic Neural Network
In this paper, our primary objective is to present an AI-based model designed for the detection of breast cancer through the analysis of microwave data. The data utilized in this study were collected by MammoWave scanner, with the magnitude of the \mathbf{S 2 1} complex number serving as the raw dat...
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| Vydáno v: | International Symposium on Medical Information and Communication Technology (Online) s. 47 - 52 |
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| Jazyk: | angličtina |
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IEEE
15.05.2024
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| ISSN: | 2326-8301 |
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| Abstract | In this paper, our primary objective is to present an AI-based model designed for the detection of breast cancer through the analysis of microwave data. The data utilized in this study were collected by MammoWave scanner, with the magnitude of the \mathbf{S 2 1} complex number serving as the raw data input. We employed an Auto Encoder-Decoder for feature extraction, followed by the utilization of a Probabilistic Neural Network for the detection of malignant lesions. The results obtained indicate that although there is an imbalance in the distribution between healthy and non-healthy classes, our proposed method demonstrates the ability to address this challenge effectively through fine-tuning. Our approach achieves an approximate 60 \% rate for accuracy, sensitivity, and specificity, showcasing its promising potential in breast cancer detection. |
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| AbstractList | In this paper, our primary objective is to present an AI-based model designed for the detection of breast cancer through the analysis of microwave data. The data utilized in this study were collected by MammoWave scanner, with the magnitude of the \mathbf{S 2 1} complex number serving as the raw data input. We employed an Auto Encoder-Decoder for feature extraction, followed by the utilization of a Probabilistic Neural Network for the detection of malignant lesions. The results obtained indicate that although there is an imbalance in the distribution between healthy and non-healthy classes, our proposed method demonstrates the ability to address this challenge effectively through fine-tuning. Our approach achieves an approximate 60 \% rate for accuracy, sensitivity, and specificity, showcasing its promising potential in breast cancer detection. |
| Author | Ghavami, Navid Tiberi, Gianluigi Badia, Mario Fracassini, Arianna Papini, Lorenzo Palomba, Gianmarco Bigotti, Alessandra Ghavami, Mohammad Taghipour-Gorjikolaie, Mehran |
| Author_xml | – sequence: 1 givenname: Mehran surname: Taghipour-Gorjikolaie fullname: Taghipour-Gorjikolaie, Mehran email: mehran.taghipour-gorjikolaie@lsbu.ac.uk organization: London South Bank University,School of Engineering,London,UK – sequence: 2 givenname: Navid surname: Ghavami fullname: Ghavami, Navid organization: UBT - Umbria Bioengineering Technologies,Perugia,Italy – sequence: 3 givenname: Gianluigi surname: Tiberi fullname: Tiberi, Gianluigi organization: London South Bank University,School of Engineering,London,UK – sequence: 4 givenname: Mario surname: Badia fullname: Badia, Mario organization: UBT - Umbria Bioengineering Technologies,Perugia,Italy – sequence: 5 givenname: Lorenzo surname: Papini fullname: Papini, Lorenzo organization: UBT - Umbria Bioengineering Technologies,Perugia,Italy – sequence: 6 givenname: Arianna surname: Fracassini fullname: Fracassini, Arianna organization: UBT - Umbria Bioengineering Technologies,Perugia,Italy – sequence: 7 givenname: Alessandra surname: Bigotti fullname: Bigotti, Alessandra organization: UBT - Umbria Bioengineering Technologies,Perugia,Italy – sequence: 8 givenname: Gianmarco surname: Palomba fullname: Palomba, Gianmarco organization: UBT - Umbria Bioengineering Technologies,Perugia,Italy – sequence: 9 givenname: Mohammad surname: Ghavami fullname: Ghavami, Mohammad organization: London South Bank University,School of Engineering,London,UK |
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| Snippet | In this paper, our primary objective is to present an AI-based model designed for the detection of breast cancer through the analysis of microwave data. The... |
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| StartPage | 47 |
| SubjectTerms | Accuracy Artificial Intelligence Auto Encoder-Decoder Biological neural networks Breast cancer Encoding Feature extraction Microwave devices Microwave theory and techniques Performance evaluation Probabilistic logic Probabilistic Neural Network Sensitivity and specificity |
| Title | Microwave-based Breast Cancer Detection by using Auto Encoder-Decoder Probabilistic Neural Network |
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