Breast calcification detection based on multichannel radiofrequency signals via a unified deep learning framework

Qiao, Menyun; Fang, Zhou; Guo, Yi; Zhou, Shichong; Chang, Cai; Wang, Yuan‐Yuan

doi:10.1016/j.eswa.2020.114218

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…In another study [ 33 ], a deep learning model achieved superior performance compared to a Nakagami parameter-based classifier, showcasing the feasibility of using RF data for accurate breast mass classification. Additionally, a deep learning framework (SCD-Net) was proposed for automatic calcification detection based on RF signals [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

“…[ 40 ], a three-step image processing scheme was introduced to enhance the generalization of a deep learning model (VGG19) for breast cancer classification. The study showed improved performance on multiple datasets, emphasizing the importance of preprocessing in deep learning models for clinical applications [ [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Norouzi Ghehi,

Fallah,

Rashidi

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Norouzi Ghehi,

Fallah,

Rashidi

et al. 2024

Heliyon

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“…Qiao et al. ( 14 ) applied the YOLOv3 network to process RF signals and detect breast calcification.…”

Section: Introductionmentioning

confidence: 99%

Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer

Wang,

Jia,

Luo

et al. 2023

Front. Oncol.

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BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.

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Computer‐Aided Classification of Breast Lesions Based on US RF Time Series Using a Novel Machine Learning Approach

Arab,

Fallah,

Rashidi

et al. 2024

J of Ultrasound Medicine

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ObjectivesOne of the most promising adjuncts for screening breast cancer is ultrasound (US) radio‐frequency (RF) time series. It has the superiority of not requiring any supplementary equipment over other methods. This research aimed to propound a machine learning (ML) approach for automatically classifying benign, probably benign, suspicious, and malignant breast lesions based on the features extracted from the accumulated US RF time series.MethodsIn this article, 220 data of the aforementioned categories, recorded from 118 patients, were analyzed. The dataset, named RFTSBU, was registered by a SuperSonic Imagine Aixplorer medical/research system equipped with a linear transducer. The regions of interest (ROIs) of the B‐mode images were manually selected by an expert radiologist before computing the suggested features. Regarding time, frequency, and time‐frequency domains, 291 various features were extracted from each ROI. Finally, the features were classified by a pioneering technique named the reference classification method (RCM). Furthermore, the Lee filter was applied to evaluate the effectiveness of reducing speckle noise on the outcomes.ResultsThe accuracy of two‐class, three‐class, and four‐class classifications were respectively calculated 98.59 ± 0.71%, 98.13 ± 0.69%, and 96.10 ± 0.66% (considering 10 repetitions) while support vector machine (SVM) and K‐nearest neighbor (KNN) classifiers with 5‐fold cross‐validation were utilized.ConclusionsThis article represented the proposed approach, named CCRFML, to distinguish between breast lesions based on registered in vivo RF time series employing an ML framework. The proposed method's impressive level of classification accuracy attests to its capability of effectively assisting medical professionals in the noninvasive differentiation of breast lesions.

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Breast calcification detection based on multichannel radiofrequency signals via a unified deep learning framework

Cited by 8 publications

References 18 publications

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Evaluating the effect of tissue stimulation at different frequencies on breast lesion classification based on nonlinear features using a novel radio frequency time series approach

Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer

Computer‐Aided Classification of Breast Lesions Based on US RF Time Series Using a Novel Machine Learning Approach

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