Microwave Breast Imaging via Neural Networks for Almost Real-time Applications

Ambrosanio, Michele; Franceschini, Stefano Sellari; Pascazio, Vito; Baselice, Fabio

doi:10.48550/arxiv.2103.12522

Cited by 3 publications

(4 citation statements)

References 49 publications

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“…The advantages of CNN-based learning include improved computational efficiency, especially when solving inverse scattering problems. Examples of studies that used machine learning and deep learning neural networks to simplify the inverse problem are given in [ 102 , 118 , 119 , 120 , 121 ]. In addition, a 2D-based imaging algorithm may not work for a 3D biological object and might lead to inaccurate reconstruction results.…”

Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

et al. 2022

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Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings.

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Section: Challenges and Future Research Directionsmentioning

confidence: 99%

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

et al. 2022

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“…In this context, we developed in [11] a quantitative technique based on a regularization procedure in Lebesgue spaces for the diagnosis of cervical myelopathy. Inversion methods based on machine learning (ML) paradigms have also been recently devised [17], considering both convolutional [18] and fully connected artificial neural networks (ANNs) [19]. In particular, the applicability of a fully connected ANN for neck tumor detection has been studied, through numerical simulations, by us in [12].…”

Section: Introductionmentioning

confidence: 99%

“…Such an ANN can be in principle applied also to other parts of the body, provided that a proper training dataset is adopted. It is worth noting that other ANNs have been tried on other body parts, e.g., for breast cancer [19] and brain stroke detection [20].…”

Section: Introductionmentioning

confidence: 99%

Initial Experimental Tests of an ANN-Based Microwave Imaging Technique for Neck Diagnostics

Dachena

Fedeli

Fanti

et al. 2022

IEEE Microw. Wireless Compon. Lett.

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In this letter, a microwave imaging strategy based on an artificial neural network (ANN) is applied, for the first time, to experimental data gathered from simplified neck phantoms. The ANN is used for solving the underlying inverse scattering problem, with the aim of retrieving the dielectric properties of the neck for monitoring and diagnostic purposes. The ANN is trained using simulated phantoms, to overcome the limited availability of experimental data. First, a simple configuration with a liquid-filled glass beaker is tested. Then, simplified 3-Dprinted models of the human neck are considered. The preliminary findings indicate the possibility of training the network with numerical simulations and testing it against experimental measurements.

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“…So, Machine learning algorithms are applied to microwave imaging in various stages like segmentation, image classification, and clustering, etc. Here, Machine learning algorithm is applied to detect the presence of tumor in the brain using microwave measurement data directly without running complex image reconstruction algorithms [12][13] [14]. The measurements are taken at multiple positions along with the head phantom.…”

Section: Introductionmentioning

confidence: 99%

Novel method of Characterization of dispersive properties of heterogeneous head tissue using Microwave sensing and Machine learning Algorithms

Lalitha

Manjula

2022

AEM

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A brain tumor is a critical medical condition and early detection is essential for a speedy recovery. Researchers have explored the use of electromagnetic waves in the microwave region for the early detection of brain tumor. However, clinical adoption is not yet realized because of the low resolution of microwave images. This paper provides an innovative approach to improve microwave brain tumor detection intelligently by differentiating normal and malignant tissues using machine learning algorithms. The dataset required for classification is obtained from the antenna measurements. To facilitate the measurement process, an Antipodal Vivaldi antenna with the diamond-shaped parasitic patch (37 mmx21 mm) is designed to operate with a resonance frequency of 3 GHz. The proposed antenna maintains a numerical reflection coefficient (S11) value below -10dB over the entire UWB frequency range. In this paper, Waikato Environment for Knowledge Analysis (WEKA) classification tool with 10 cross-fold validation is used for comparison of various algorithms against the dataset obtained from the proposed antenna.

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Microwave Breast Imaging via Neural Networks for Almost Real-time Applications

Cited by 3 publications

References 49 publications

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

Initial Experimental Tests of an ANN-Based Microwave Imaging Technique for Neck Diagnostics

Novel method of Characterization of dispersive properties of heterogeneous head tissue using Microwave sensing and Machine learning Algorithms

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