## 4 Experimental Results
### Classification Results
#### 4.4.2 Performance Evaluation Using Different Datasets
\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}{**Class**} & \multicolumn{5}{c}{**No. of Images**} & \multicolumn{5}{c}{**Percentage (\%)**} \\ \hline
**TR** & **TS** & **BN** & **MA** & **TN** & **TP** & **FN** & **FP** & **Acc** & **Sn** & **Sp** & **F-Measure** \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Mini-MIAS \\ \end{tabular} } & LBP & \multirow{5}{*}{479} & LBP & \multirow{5}{*}{127} & 398 & 384 & 104 & 95 & 79.71 & 78.68 & 80.73 & 78.9 \\ \cline{3-11}  & & FD & & 405 & 397 & 97 & 82 & 81.75 & 80.36 & 83.16 & 81.6 \\ \cline{3-11}  & & Proposed M-FD & & & 501 & 479 & 1 & 0 & 99.89 & 99.79 & 100 & 99.89 \\ \cline{3-11} \cline{3-11}  & & LBP & & & 49 & 113 & 24 & 14 & 81 & 82.48 & 77.77 & 85.6 \\ \cline{3-11}  & & FD & & & 50 & 116 & 23 & 11 & 83 & 83.45 & 81.96 & 87.21 \\ \cline{3-11}  & & Proposed M-FD & & & 68 & 126 & 5 & 1 & 97 & 96.18 & 98.55 & 97.67 \\ \cline{3-11} \cline{3-11}  & & LBP & & & 211 & 334 & 99 & 92 & 74.04 & 77.13 & 69.63 & 77.76 \\ \cline{3-11}  & & FD & & & 215 & 338 & 95 & 88 & 75.13 & 78.06 & 70.95 & 78.69 \\ \cline{3-11}  & & Proposed M-FD & & & 308 & 425 & 2 & 1 & 99.59 & 99.53 & 99.67 & 99.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation obtained on the test data for benign or malignant classification using Mini-MIAS as training data. \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}{**Class**} & \multicolumn{5}{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}{*}{DDSM} \\ \end{tabular}  } & LBP & \multirow{5}{*}{206} & \multirow{5}{*}{110} & 79 & 187 & 31 & 19 & 84.17 & 85.77 & 80.61 & 88.2 \\ \cline{3-11}  & & FD & & 84 & 188 & 26 & 18 & 86.07 & 87.85 & 82.35 & 89.52 \\ \cline{3-11}  & & Proposed M-FD & & & 109 & 204 & 1 & 2 & 99.05 & 99.51 & 98.19 & 99.27 \\ \cline{3-11} \cline{3-11}  & & LBP & & & 50 & 115 & 23 & 12 & 82.5 & 83.33 & 80.64 & 86.79 \\ \cline{3-11}  & & FD & & & 51 & 116 & 22 & 11 & 83.5 & 84.05 & 82.25 & 87.54 \\ \cline{3-11}  & & Proposed M-FD & & & 71 & 125 & 2 & 2 & 98 & 98.42 & 97.26 & 98.42 \\ \cline{3-11} \cline{3-11}  & & LBP & & & 231 & 349 & 79 & 77 & 78.8 & 81.54 &Table 4 shows the performance analysis for benign or malignant classification of the proposed M-FD method with traditional FD and traditional LBP. In this evaluation, the Mini-MIAS dataset has been used in the training stage whereas other datasets have been used for the testing stage. From Mini-MIAS a total of 250 samples including 162 benign and 88 malignant are taken in the training stage. A total of 981, 200, and 736 samples from DDSM, INbreast, and BCDR, respectively, are taken for testing. It is seen from the results that the DDSM obtained 99.89% for accuracy, 99.79% sensitivity, 100% specificity, and 99.89% F-measure. Moreover, BCDR obtained 99.59%, 99.53%, 99.67%, and 99.64% for accuracy, sensitivity, specificity, and F-measure. Results of DDSM and BCDR outperformed the previous evaluation (single-dataset evaluation) in this evaluation. DDSM and BCDR achieved higher results in terms of specificity. However, the INbreast dataset obtained higher results in the previous evaluation (single-dataset evaluation) by achieving 97% accuracy, 96.18% sensitivity, 98.55% specificity, and 97.67% F-measure. Table 5 shows performance for benign and malignant classification when DDSM is used as a training dataset. A total of 654 samples from DDSM are taken for training including 319 benign and 335 malignant. However, 316 samples from Mini-MIAS, 200 samples from INbreast, and 736 samples from BCDR are used in the testing stage. It is observed that Mini-MIAS obtained higher results from the single-dataset evaluation, achieving 99.05% accuracy, 99.51% sensitivity, 98.19% specificity, and 99.27% F-measure.