## 4 Experimental Results
### Classification Results
#### 4.4.2 Performance Evaluation Using Different Datasets
Moreover, in this evaluation, BCDR obtained higher results from single-dataset evaluation as well for accuracy 97.41%, sensitivity 98.33%, specificity 96.16%, and F-measure 97.75% whereas the INbreast dataset still outperformed in single-dataset evaluation by obtaining 98%, 98.42%, 97.26%, and 98.42% for accuracy, sensitivity, specificity, and F-measure, respectively. Table 6 shows that in the INbreast dataset used in the training stage, a total of 159 samples with 101 benign and 58 malignant are taken to train the proposed model. Table 6 shows that 316 samples are taken from Mini-MIAS for testing, the proposed M-FD achieved 99.68% accuracy, 100% sensitivity, 99.09% specificity, and 99.75% F-measure. From the DDSM dataset, 981 samples are taken for testing, and the classification accuracy and F-measure obtained were 98.16% and 98.12%, respectively while DDSM obtained 97.92% and 98.4% classification sensitivity and specificity, respectively. Furthermore, a total of 736 samples are taken from BCDR to test the proposed model. The performance of the classification evaluation achieved 98.64% and 99.28% for accuracy and sensitivity while obtaining 97.77% and 98.82% for specificity and F-measure, respectively. Finally, this study evaluates the proposed M-FD by taking the BCDR dataset as training data. A total of 491 samples including 284 benign and 207 malignant are taken in the training stage. Table 7 depicts the result achieved with the proposed M-FD on the Mini-MIAS, DDSM, and INbreast datasets. M-FD yields better evaluation performance by achieving 100% accuracy, sensitivity, specificity, and F-measure. The classification accuracies for DDSM and INbreast datasets are 99.49% and 96.5%, respectively. The testing \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Database**} & \multirow{2}{*}{**Feature**} & \multicolumn{3}{c}{**Class**} & \multicolumn{3}{c}{**No. of Images**} & \multicolumn{3}{c}{**Percentage (\%)**} \\ \cline{3-11} \multicolumn{2}{c}{**TR**} & & **TS** & **BN** & **MA** & **TN** & **TP** & **FN** & **FP** & **Ac** & **Sn** & **Sp** & **F-Measure** \\ \hline \multirow{8}{*}{BCDR} & \multirow{4}{*}{Mini-MIAS} & LBP & \multirow{4}{*}{206} & \multirow{4}{*}{110} & 83 & 194 & 25 & 14 & 83.61 & 88.58 & 85.56 & 90.86 \\ \cline{3-11}  & & FD & & & 87 & 196 & 23 & 10 & 89.55 & 89.49 & 89.69 & 92.23 \\ \cline{3-11}  & & \multirow{4}{*}{Proposed M-FD} & & & 110 & 206 & 0 & 0 & 100 & 100 & 100 & 100 \\ \cline{3-11}  & & LBP & \multirow{4}{*}{479} & \multirow{4}{*}{502} & 394 & 382 & 108 & 93 & 79.1 & 77.95 & 80.9 & 79.17 \\ \cline{3-11}  & & FD & & & 399 & 391 & 103 & 88 & 80.53 & 79.14 & 81.93 & 80.36 \\ \cline{3-11}  & & Proposed M-FD & & & 497 & 479 & 5 & 0 & 99.49 & 98.96 & 100 & 99.48 \\ \cline{3-11} \cline{2-11}  & & LBP & \multirow{4}{*}{127} & & 50 & 113 & 23 & 14 & 81.5 & 83.08 & 78.12 & 85.93 \\ \cline{3-11}  & & FD & & & 50 & 114 & 23 & 13 & 82 & 83.21 & 79.36 & 86.36 \\ \cline{3-11}  & & Proposed M-FD & & & 69 & 124 & 4 & 3 & 96.5 & 96.87 & 95.83 & 97.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation obtained on the test data for benign or malignant classification using BCDR as training data. datasets obtained 98.96% and 96.87% and 100% and 95.83% for sensitivity and specificity, respectively. More so, F-measure obtained was 99.48% for DDSM and 97.25% for INbreast. Two different experiments were conducted to evaluate the performance of the proposed M-FD method, as illustrated above. Two different traditional methods (FD and LBP) were used for feature extraction with the same datasets. Extracted features based on the traditional FD, traditional LBP, and the proposed M-FD were then fed to the ANN classifier. This evaluation and comparison showed the effectiveness of the proposed method. The results clearly showed that the traditional FD produces a better result than the traditional LBP. We then enhanced the traditional FD, and the results showed that the proposed M-FD model increases the result and improves the confidence of the system.