## 3 Experimental Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Image Database** & **Method** & **Clinical/Medical Purpose** \\ \hline \multirow{3}{*}{[24]} & MIAS Image Database [22; 23] & \multirow{3}{*}{SVM} & Mammogram Classification \\  & (322 Mammographic Images, 161 Subjects) & & Accuracy: 94\% \\ \hline \multirow{3}{*}{[25]} & MIAS Image Database [22; 23] & \multirow{3}{*}{ANN} & Mass Detection \\  & (322 Mammographic Images, 161 Subjects) & & Recognition Rate: 97.08\% \\ \hline \multirow{3}{*}{[27]} & MIAS Image Database [22; 23] & \multirow{3}{*}{Clustering Method: K-Means and Fuzzy C-Means} & Mass Segmentation \\  & (322 Mammographic Images, 161 Subjects) & & K-Means: 91.18\% \\ \hline \multirow{3}{*}{[59]} & IRMA Database [21] & \multirow{3}{*}{SVM} & Abnormality Detection \\  & (11,000 X-ray images) & & IRMA: Sensitivity: 99\%; Specificity: 99\% \\  & DDSM Database [19] & & DDSM: Sensitivity: 97\%; Specificity: 96\% \\  & (2620 Enrolled Subjects) & & \\ \hline \multirow{3}{*}{[60]} & E-Da Hospital Image Database & \multirow{3}{*}{DNN-based Classifier} & BI-RADS Classification (8 Classes) \\  & (5733 Mammographic Images, 1490 Subjects) & & Sensitivity: 95.31\%; Specificity: 99.15\%; \\  & & & Accuracy: 94.22\% \\ \hline \multirow{3}{*}{[61]} & DDSM Database [19] & GA (Genetic & BI-RADS 2-5 Classification (4 Classes) \\  & (500 Images) & Algorithm)-based & Accuracy: 84.5\%; Positive Predictive Value: 84.4\%; Negative Predictive Value: 94.8\%; [PERSON] Correlation Coefficient: 79.3\% \\ \hline \multirow{3}{*}{[37]} & DDSM [19; 62], INbreast [63], and MIAS [22; 23] Database & TTCNN & (2) For INbreast Sensitivity: 97.68\%; Specificity: 95.99\%; Accuracy: 96.82\% \\  & & & (3) For MIAS: Sensitivity: 96.11\%; Specificity: 97.03\%; Accuracy: 96.57\% \\ \hline \multirow{3}{*}{[38]} & Collected by Department of & \multirow{3}{*}{} & Automated Breast Cancer Detection \\  & Breast and Endocrine Surgery & & (1) DenseNet-169; AUC = 0.952 \(\pm\) 0.005; \\  & of Hallym University Saced & \multirow{3}{*}{} & Mean Sensitivity: 87.0\%; Mean Specificity: 88.4\%; Mean Accuracy: 88.1\% \\  & Heart Hospital [38] & \multirow{3}{*}{} & (2) EfficientNet-B5: AUC = 0.954 \(\pm\) 0.020; Mean \\  & (1501 subjects, 2007–2015 years) & & Sensitivity: 88.3\%; Mean Specificity: 87.9\%; Mean Accuracy: 87.9\% \\ \hline \multirow{3}{*}{[26]} & Private Hospital Image & \multirow{3}{*}{} & Breast Density Estimation \\  & Database [26] & \multirow{3}{*}{FCN Model} & Pearson’s Rho Values: Mediolateral Oblique \\  & (Medolateral Oblique View: 1208 Images; Craniocalat & & View: 0.81; Craniocalat View: 0.79 \\  & View: 1208 Images) & & DDSM: Dice Similarity Coefficient: 0.915 \(\pm\) 0.031 \\ \hline \multirow{3}{*}{[28]} & DDSM Database [19] & \multirow{3}{*}{Attention Dense—Unet Model} & Mass Segmentation \\  & (2620 Enrolled Subjects) & & Sensitivity: 77.89\%; Specificity: 84.69\%; \\  & & & Accuracy: 78.38\% \\ \hline \multirow{3}{*}{[29]} & CBIS-DDSM Database [20] & \multirow{3}{*}{Dense—Unet Model} & \multirow{3}{*}{Calcification Detection} \\  & & & Sensitivity: 91.22\%; Specificity: 92.01\%; \\  & & & Accuracy: 91.47\%; F1 Score: 0.9219 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Proposed \\ Method \\ \end{tabular} } & MIAS Image Database [22; 23] & \multirow{3}{*}{2D Spatial Fractional-Order} \\  & (322 Mammographic Images, 161 Subjects) & & Convolutional Process + \\ \cline{1-1} \cline{3-4}  & (322 Mammographic Images, 161 Subjects) & Two-round 1D Convolutional \\ \cline{1-1} \cline{3-4}  & 161 Subjects & Processes + GRA-based Fully \\ \cline{1-1} \cline{3-4}  & & Connected Network \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the cardiomegaly level screening methods.