## 4 Experimental Results
### Classification Results
#### 4.4.2 Performance Evaluation Using Different Datasets  
The performance of a single testing process using a double dataset is evaluated in this section. The double dataset (two different databases) was used in a single evaluation. The performance evaluation is measured by taking training samples from one dataset and testing samples from another dataset. The main reason behind this evaluation strategy is to ensure that the proposed M-FD model can work using different datasets. Traditional \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Feature**} & \multicolumn{2}{c}{**Class**} & \multicolumn{3}{c}{**No. of Images**} & \multicolumn{3}{c}{**Percentage (\%)**} \\ \cline{3-11}  & & **BN** & **MA** & **TN** & **TP** & **FN** & **FP** & **AC** & **Sn** & **Sp** & **F-Measure** \\ \hline \multirow{4}{*}{Mini-MIAS} & LBP & & & 73 & 177 & 37 & 29 & 79.11 & 82.71 & 71.56 & 84.28 \\  & FD & 206 & 110 & 76 & 177 & 34 & 29 & 80.06 & 83.88 & 72.38 & 84.89 \\  & Proposed M-FD & & & 103 & 201 & 7 & 5 & 96.2 & 96.63 & 95.37 & 97.1 \\ \hline \multirow{4}{*}{DDSM} & LBP & & & 397 & 380 & 105 & 99 & 79.2 & 78.35 & 80.04 & 78.83 \\  & FD & 479 & 502 & 401 & 393 & 101 & 86 & 80.93 & 79.55 & 82.34 & 80.78 \\  & Proposed M-FD & & & 494 & 473 & 8 & 6 & 98.57 & 98.33 & 98.8 & 98.54 \\ \hline \multirow{4}{*}{INbreast} & LBP & & & 50 & 115 & 23 & 12 & 82.5 & 83.33 & 80.64 & 86.79 \\  & FD & 127 & 73 & 52 & 119 & 21 & 8 & 85.5 & 85 & 86.66 & 89.13 \\  & Proposed M-FD & & & 71 & 127 & 2 & 0 & 99 & 98.44 & 100 & 99.21 \\ \hline \multirow{4}{*}{BCDR} & LBP & & & 208 & 329 & 102 & 97 & 72.96 & 76.33 & 68.19 & 76.77 \\  & FD & 426 & 310 & 211 & 333 & 99 & 93 & 73.91 & 77.08 & 69.4 & 77.62 \\ \cline{1-1}  & Proposed M-FD & & & 304 & 416 & 6 & 10 & 97.82 & 98.57 & 96.81 & 98.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: First experiment of mammogram classification results as benign or malignant using different databases. FD and LBP features are also used in the double-dataset evaluation. The proposed M-FD model outperformed both traditional FD and LBP features in all evaluations when Mini-MIAS, DDSM, INbreast, and BCDR datasets were used in the training stage, as shown in Tables 4-7, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Database**} & \multicolumn{2}{c}{**Feature**} & \multicolumn{2}{c}{**Class**} & \multicolumn{2}{c}{**No. of Images**} & \multicolumn{5}{c}{**Percentage (\%)**} \\ \hline
**TR** & **TS** & **BN** & **MA** & **TN** & **TP** & **FN** & **FP** & **Ac** & **Sn** & **Sp** & **F-Measure** \\ \hline \multirow{5}{*}{
\begin{tabular}{c} \multirow{-2}{*}{INbreast} \\ \end{tabular} } & \multirow{5}{*}{Mini-MIAS} & LBP & \multirow{5}{*}{206} & \multirow{5}{*}{110} & 84 & 193 & 26 & 13 & 87.65 & 88.12 & 86.59 & 90.82 \\ \cline{3-11}  & & & FD & & 89 & 196 & 21 & 10 & 90.10 & 90.32 & 89.89 & 92.67 \\ \cline{3-11}  & & \multirow{2}{*}{Proposed M-FD} & & & 110 & 205 & 0 & 1 & 99.68 & 100 & 99.09 & 99.75 \\ \cline{3-11}  & & & LBP & & 392 & 380 & 110 & 99 & 78.69 & 77.55 & 79.83 & 78.43 \\ \cline{3-11}  & & \multirow{2}{*}{PDSM} & \multirow{2}{*}{FD} & \multirow{2}{*}{479} & \multirow{2}{*}{502} & 396 & 390 & 106 & 89 & 80.12 & 78.62 & 81.64 & 80 \\ \cline{3-11}  & & & Proposed M-FD & & & 492 & 471 & 10 & 8 & 98.16 & 97.92 & 98.4 & 98.12 \\ \cline{3-11} \cline{3-11}  & & \multirow{2}{*}{BCDR} & LBP & \multirow{2}{*}{426} & \multirow{2}{*}{310} & 236 & 356 & 74 & 70 & 80.43 & 82.79 & 77.12 & 83.17 \\ \cline{3-11}  & & & FD & & 246 & 357 & 64 & 69 & 81.92 & 84.79 & 78.09 & 84.29 \\ \cline{3-11}  & & \multirow{2}{*}{Proposed M-FD} & & & 307 & 419 & 3 & 7 & 98.64 & 99.28 & 97.77 & 98.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation obtained on the test data for benign or malignant classification using INbreast as training data.