Classification Of Benign And Malignant Breast Lesions Based On Ultrasound Radio Frequency Time Series

Breast cancer is the most common malignant tumor in women,which seriously endangers women's life and health.Biopsy is the "gold standard" for the diagnosis of benign and malignant breast lesions.However,biopsy is invasive,and there are too many unnecessary biopsy in clinical practice,so it is necessary to have a high accuracy and noninvasive classification of breast lesions to provide effective diagnostic information for clinicians to reduce unnecessary biopsy.In this paper,the ultrasound frequency(RF)time series is used as the data source,and the breast lesions are studied as samples,feature extraction is carried out from four dimensions: time domain,frequency domain,time-frequency domain and nonlinear dynamics,with support vector machine(SVM)and random forest as classifiers,two high-precision and non-invasive methods for classifying benign and malignant breast lesions are proposed,and developed a computer aided diagnosis(CAD)system for the classification of benign and malignant breast lesions.Solution 1 extracted the features of ultrasonic RF time series from the three dimensions of time domain,frequency domain and nonlinear dynamics.the area under receiver operating characteristic curve(AUC)of the SVM was 0.914,the best accuracy rate was 86.59%,and the average accuracy rate was 80.08%,the AUC of random forest was 0.937,the best accuracy rate was 95.12%,the average accuracy was 87.33%.Compared with the best results in current study,the AUC of SVM and random forest were increased by 1.6% and 10.2%,respectively.Solution 2 firstly decomposes the ultrasonic RF time series with three layers of Mallat wavelet,then extracts the statistical and nonlinear dynamic features of the low frequency coefficients and high frequency coefficients in the time-frequency domain,then the frequency domain features of solution one are improved and extracted,and finally uses the Relief-F algorithm and Principal Component Analysis(PCA)to reduce the dimension of the features.Relief-F algorithm was chosen as the dimension reduction method.The AUC of SVM was 0.932,the best accuracy rate was 86.67%,and the average accuracy rate was 82.22%,the AUC of random forest was 0.997,the best accuracy rate was 98.67%,and the average accuracy,average sensitivity,and average specificity were 94.46%,94.92%,and 95.25%,respectively.compared with the best results in current study,the AUC of SVM and random forest increased by 3.6% and 17.3%,respectively;PCA was chosen as the dimension reduction method.The AUC of SVM was 0.915,the best accuracy rate was 88.75%,and the average accuracy rate was 82.22%,the AUC of random forest was 0.985,the best accuracy rate was 96.25%,the average accuracy rate was 92.5%,the average sensitivity was 94.58%,and the average specificity was 89.78%.compared with the best results in current study,the AUC of SVM and random forest increased by 1.7% and 15.9%,respectively.At the same time,the(CAD)system for the classification of benign and malignant breast lesions was developed under Visual Studio 2013,which can provide clinicians with effective auxiliary diagnostic information,effectively reduce unnecessary biopsy,and provide a noninvasive solution for the identification of benign and malignant breast lesions.