Abstract:(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast … Show more
“…Half of the research in image-based patient diagnosis has been performed using subjective diagnostic criteria [49,111,141,143,170]. These articles relied on subjective judgments of diagnosticians to determine the presence of a cancerous response in the reconstructed image.…”
Section: Estimates Of the Diagnostic Potential Of Image-based Tumour ...mentioning
confidence: 99%
“…Subjective diagnostic criteria have been commonly used. [100,111,120,141,143,170,201], subjective diagnosis [49,111,141,143,170], and sample size limitations were common [48,143,201].…”
Section: Subjective Diagnosismentioning
confidence: 99%
“…These studies are mentioned here for completeness as there are relatively few applications of machine learning for tumour detection within breast microwave sensing. Other studies, which classified known lesions in patient data [48,49,93,196], were included due to their use of patient data.…”
Section: Analysis Of Existing Estimates Of the Diagnostic Performance...mentioning
confidence: 99%
“…The studies that were selected for possible inclusion based on their abstract and title (n = 468) were then evaluated with respect to the inclusion and exclusion criteria. One hundred seventy-five papers were identified for inclusion in this review [11][12][13][14][24][25][26][27][28][29][30][31][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][68][69][70][71][181]…”
mentioning
confidence: 99%
“…Current challenges in image-based estimation of the diagnostic performance of BMS. Nonblind studies[100,111,120,141,143,170,201], subjective diagnosis[49,111,141,143,170], and sample size limitations were common[48,143,201].…”
This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast—other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.
“…Half of the research in image-based patient diagnosis has been performed using subjective diagnostic criteria [49,111,141,143,170]. These articles relied on subjective judgments of diagnosticians to determine the presence of a cancerous response in the reconstructed image.…”
Section: Estimates Of the Diagnostic Potential Of Image-based Tumour ...mentioning
confidence: 99%
“…Subjective diagnostic criteria have been commonly used. [100,111,120,141,143,170,201], subjective diagnosis [49,111,141,143,170], and sample size limitations were common [48,143,201].…”
Section: Subjective Diagnosismentioning
confidence: 99%
“…These studies are mentioned here for completeness as there are relatively few applications of machine learning for tumour detection within breast microwave sensing. Other studies, which classified known lesions in patient data [48,49,93,196], were included due to their use of patient data.…”
Section: Analysis Of Existing Estimates Of the Diagnostic Performance...mentioning
confidence: 99%
“…The studies that were selected for possible inclusion based on their abstract and title (n = 468) were then evaluated with respect to the inclusion and exclusion criteria. One hundred seventy-five papers were identified for inclusion in this review [11][12][13][14][24][25][26][27][28][29][30][31][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][68][69][70][71][181]…”
mentioning
confidence: 99%
“…Current challenges in image-based estimation of the diagnostic performance of BMS. Nonblind studies[100,111,120,141,143,170,201], subjective diagnosis[49,111,141,143,170], and sample size limitations were common[48,143,201].…”
This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast—other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.
In this paper, a modern and effective approach with high resilience for microwave imaging of breast tumor detection using 12 antennas is presented. The proposed antenna array operates from 3.1 GHz to 11.6 GHz according to FCC frequencies, which enables the imaging system to mix two advantages at the same time, deep penetration, and high resolution for accurate image acquisition. The suggested antenna array has a compact size of 21 × 21 × 12 cm3, this allows for the integration of as many elements as feasible, provided that the coupling between them remains within an acceptable range. This ensures improved accuracy and resolution in the resulting image. The 3D system is arranged in a circular manner around the phantom to cover all breast positions. A measurement campaign will then be made in different scenarios to detect the tumor using two artificial breasts with and without tumor, their dielectric properties and physiological arrangement approximate those of a natural breast. The proposed microwave imaging system can detect and identify all targets, making it one of the most effective systems for detecting breast tumors.
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