Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

Relationship between breast arterial calcification on mammography with coronary CT angiography findings

BACKGROUND： Breast arterial calcification is a frequently benign finding on mammography that usually is not reported. An increasing attention has been formed to determine the association between breast arterial calcification （BAC） and Coronary artery disease. In the current study we have aimed to evaluate the relationship between BAC on mammography with coronary CT angiography findings. METHODS： The case control study was carried out on 60 women; 30 CAD and 30 healthy subjects as control, admitted to Golestan hospital radiology department. The mammography was performed in two views; Craniocaudal （CC） view and mediolateral oblique （MLO） and BAC was graded based on the severity and extent of calcifications. Coronary Arterial Calcification （CAC） were scored by Agatston criteria. RESULTS： Overly, 36 patients （60%） were positive for BAC. Twenty six out of them （72%） were CAD. There was a positive significant correlation between BAC and CAD. The sensitivity and specificity of BAC for CAD were 69% and 47%, respectively. Moreover, the severe BAC scores were significantly higher in CAD patients than non-CAD. CONCLUSION： Our findings in line with several previous studies indicated the positive significant association between BAC and CAD occurrence. While the sensitivity and specificity of BAC in diagnosis of CAD is low, suggested the using of BAC just as a CAD risk factor. The relatively low sample size is the major limitation of the study.