A comparison of data-independent microwave beamforming algorithms for the early detection of breast cancer

A Novel Approach of a Low-Cost UWB Microwave Imaging System with High Resolution Based on SAR and a New Fast Reconstruction Algorithm for Early-Stage Breast Cancer Detection

In this article, a new efficient and robust approach-the high-resolution microwave imaging system-for early breast cancer diagnosis is presented. The core concept of the proposed approach is to employ a combination of a newly proposed delay-and-sum (DAS) algorithm and the specific absorption rate (SAR) parameter to provide high image quality of breast tumors, along with fast image processing. The new algorithm enhances the tumor response by altering the parameter referring to the distance between the antenna and the tumor in the conventional DAS matrices. This adjustment entails a much clearer reconstructed image with short processing time. To achieve these aims, a high directional Vivaldi antenna is applied around a simulated hemispherical breast model with an embedded tumor. The detection of the tumor is carried out by calculating the maximum value of SAR inside the breast model. Consequently, the antenna position is relocated near the tumor region and is moved to nine positions in a trajectory path, leading to a shorter propagation distance in the image-creation process. At each position, the breast model is illuminated with short pulses of low power waves, and the back-scattered signals are recorded to produce a two-dimensional image of the scanned breast. Several simulations of testing scenarios for reconstruction imaging are investigated. These simulations involve different tumor sizes and materials. The influence of the number of antennas on the reconstructed images is also examined. Compared with the results from the conventional DAS, the proposed technique significantly improves the quality of the reconstructed images, and it detects and localizes the cancer inside the breast with high quality in a fast computing time, employing fewer antennas.

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.

A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection

In this paper, a compact, flexible Vivaldi antenna is designed, and an array of nine identical antennas of this type is used as a microwave breast imaging model to detect cancerous tumors in the multilayers phantom model presented in this paper. The nine-antenna array is used to measure the backscattering signal of the breast phantom, where one antenna acts as a transmitter and the other eight antennas act as receivers of the scattered signals. Then, the second antenna is used as a transmitter and the other antennas as receivers, and so on till we have gone through all the antennas. These collected backscattered signals are used to reconstruct the image of the breast phantom using software called “Microwave Radar-based Imaging Toolbox (MERIT)”. From the reconstructed image, the tumor inside the breast model can be identified and located. Different tumor sizes in different locations are tested, and it is found that the locations can be determined irrespective of the tumor size. The proposed modified Vivaldi antenna has a very compact size of 25 × 20 × 0.1 mm3 and has a different geometry compared with conventional Vivaldi antennas. The first version of the antenna has two resonant frequencies at 4 and 9.4 GHz, and because we are interested more in the first band, where it gives us sufficient resolution, we have notched the second frequency by etching two slots in the ground plane of the antenna and adding two rectangular parasitic elements on the radiating side of the antenna. This technique is utilized to block the second frequency at 9.4 GHz, and, as a result, the bandwidth of the first resonant frequency is enhanced by 20% compared with the first design bandwidth. The modified antenna is fabricated on Polyimide flexible material 0.1 mm thick with a dielectric constant of 3.5 using a standard PCB manufacturing process. The measured performance of this antenna is compared with the simulated results using the commercially available simulation software Ansoft HFSS, and it is found that the measured results and the simulated results are in good agreement.

Review of Microwaves Techniques for Breast Cancer Detection

Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.