## 4 Experimental Results
### Comparision with Other Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification Methods** &
\begin{tabular}{c} **Total No. of** \\ **Images** \\ \end{tabular} & **Classifier** & **Feature** & **Ac (\%)** & **Sn (\%)** & **Sp (\%)** & **F-Measure (\%)** \\ \hline
[50] & 981 & SVM &
\begin{tabular}{c} Contourlet + FOA (45 features) \\ \end{tabular} & 98.18 & - & - & - \\ \hline
[57] & 72 & Ensemble &
\begin{tabular}{c} Hybrid LB-GLCM + LPQ \\ (texture) \\ \end{tabular} & 93.1 & 95 & 88 & - \\ \hline
[58] & 981 & MFO-ELM & LWT + PCA (32 features) & 98.8 & 96.2 & 97.66 & - \\ \hline
[59] & 300 & ELM &
\begin{tabular}{c} Gray level and textural \\ features, 32 \\ \end{tabular} & 95.73 & 94.88 & 97.16 & - \\ \hline
[60] & 300 & FGMM & Intensity, shape, texture & 93 & 90 & 96 & - \\ \hline
[62] & 981 & WC-SSA &
\begin{tabular}{c} Block-based discrete \\ wavelet packet transforms \\ \end{tabular} & 99.63 & 99.77 & 99.37 & 99.45 \\ \hline
[63] & 250 & ANN &
\begin{tabular}{c} GLCM, GLRLM, wavelet \\ (50 texture and shape) \\ \end{tabular} & 98.9 & 100 & 97.8 & - \\ \hline
[64] & 287 & ANN &
\begin{tabular}{c} GLRL, GLSZ, NGTD, \\ LTEM (13 histogram, \\ texture, 18 shape) \\ \end{tabular} & 96.2 & 97.6 & 95.2 & - \\ \hline
[65] & 2781 & AdaBoost & \
\begin{tabular}{c} SIFT, GIST, HOG, LBP, \\ VGG16, ResNet, and \\ DenseNet (shape, texture, \\ and deep learning) \\ \end{tabular} & 90.91 & 82.96 & 98.38 & - \\ \hline Proposed M-FD model & 981 & ANN &
\begin{tabular}{c} LBP \\ FD \\ M\_FD \\ \end{tabular} & 79.71 & 78.68 & 80.9 & 79.17 \\ \hline
[65] & 2781 & AdaBoost &
\begin{tabular}{c} FD \\ M\_FD \\ \end{tabular} & 99.89 & 99.79 & 100 & 99.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison with respect to DDSM dataset. Best values are shown in bold. Moreover, because sensitivity deals with positive cases, when the TP rate is low it means the sensitivity rate is high. This means that the sensitivity is used to identify the risk of benign; thus, when the number of benign images is low, the sensitivity rate will be high. On contrary, for the DDSM dataset, our proposed model exceeds all other previous studies in terms of all used matrices with using a high number of images in our evaluation. In Table 10, we present a comparative study between our proposed model and two recent previous studies in INbreast and BCDR datasets. Our proposed model outperformed in terms of accuracy, sensitivity, specificity, and F-measure compared to [65] despite using texture, shape, and deep learning features. However, the previous study of [65] used 387 images, whereas we evaluate 200 images in the INbreast dataset. Furthermore, the proposed model yields better performance in terms of specificity whereas the study [62] has better results in terms of accuracy, sensitivity, and F-measure. The proposed model also provides better evaluation performance when compared to the study [62] in Mini-MIAS and DDSM datasets. Our proposed model gives accuracy, sensitivity, and F-measure of 99.59%, 99.53%, and 99.64%, respectively, while [62] obtained 99.6%, 100%, and 99.7%, respectively. This means that the previous study of [62] outperformed in terms of accuracy, sensitivity, and F-measure slightly compared to our proposed model. In contrast, our proposed study outperformed in terms of specificity by obtaining 99.67% while the previous study yields 99.35% (see Table 10). As a result, it is observed that our proposed model yields state-of-the-art evaluation performance compared to several previous studies in the Mini-MIAS, DDSM, INbreast, and BCDR. Therefore, our proposed model can work with different datasets efficiently while outperforming the previous study of [61] slightly on the BCDR dataset. Table 11 compares the proposed method with deep learning methods investigated in [66]. DenseNet and DenseNet-II models were compared with our proposed model. We have used the same models for the Mini-MIAS, DDSM, INbreast, and BCDR datasets to show the effectiveness of deep learning models compared with the proposed model.