Ultra-Wideband (UWB) Antenna Sensor Based Microwave Breast Imaging: A Review

Mahmud, Zuria; Islam, Mohammad Tariqul; Misran, Norbahiah; Almutairi, Ali F.; Cho, Mengu

doi:10.3390/s18092951

Cited by 52 publications

(31 citation statements)

References 66 publications

(66 reference statements)

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“…Fast emerging technologies have been developed and advanced by the scientists in the past few decades, to study the biophysical properties of cells and to make extensive influences on biology and the clinical research field. For the breast cancer detection, available techniques are X-ray mammography [47], magnetic resonance imaging [48], ultrasound waves [49,50], positron emission tomography [51], and ultrawideband techniques [52]. A comparison of these techniques has been done in Table 2, depicting sensitivity, specificity, positive predicted value, and accuracy.…”

Section: Electrical Characterizationmentioning

confidence: 99%

Cells Electrical Characterization: Dielectric Properties, Mixture, and Modeling Theories

Nasir

Ahmad

2020

Journal of Engineering

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Electrical detection of biotic materials and their properties is always crucial in Biomedical Engineering. It emphasizes elaborate analysis of a certain disease diagnosis using electrical means which includes the study of electrical properties followed by detection, identification, and quantification of biotic entities. Proper and early detection of cells, which can be normal or cancerous, holds never-ending demand. This review emphasizes the electrical characterization methods in modeling and classification of cell properties. Moreover, standard methods have been highlighted and discussed to yield performance analysis. This study suggests that techniques presented for single-cell analysis (SCA) are always precise and hold great scope compared to those for cell mixture. SCA plays a promising role in disease diagnosis and treatment planning, as it not only detects the cells but also helps in identification. Distinct electrical-based cell detection techniques are highlighted, along with the pros and cons of various SCA devices. The content, in terms of methodologies and available techniques, of this review paper is useful to attract researchers working in this area.

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Section: Electrical Characterizationmentioning

confidence: 99%

Cells Electrical Characterization: Dielectric Properties, Mixture, and Modeling Theories

Nasir

Ahmad

2020

Journal of Engineering

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“…The rate of a woman contracting breast cancer is reported at a worrying rate globally, particularly in developing countries. Symptoms of breast cancer, for instance, visual changes in the breasts are usually discovered only at the final stage [ 1 ]. Consequently, most of the breast cancer cases are detected in the latter stage, at which, are deemed as too late for medical treatment, thus causing death [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction

et al. 2020

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Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

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“…The accuracy of the proposed method is compared with the existing work, [1,31] as demonstrated in Table 6. The result proves the proposed MSFS method and hybrid feature are better compared to the other existing method.…”

Section: Resultsmentioning

confidence: 99%

“…The research involving UWB imaging for breast cancer started effectively in year 1999 by S.C. Hagness from Winconsin University, USA [7], and since then, started to gain popularity among the researchers, credit to the advancement of computational power since late 1990s. Basically, researchers used either real-time machines such as a vector network analyzer or machine learning to analyze the UWB signals [1].…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

Vijayasarveswari

Andrew

Jusoh

et al. 2020

Preprint

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Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultrawideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

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Ultra-Wideband (UWB) Antenna Sensor Based Microwave Breast Imaging: A Review

Cited by 52 publications

References 66 publications

Cells Electrical Characterization: Dielectric Properties, Mixture, and Modeling Theories

Cells Electrical Characterization: Dielectric Properties, Mixture, and Modeling Theories

Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction

Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

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