## 4 Experimental Results
### Classification Results
#### 4.4.1 Performance Evaluation of Single Dataset  
Two experiments were conducted to improve the robustness of our performance evaluation. The first experiment is the single-dataset evaluation that uses stratified leave-out-one cross-validation, that is, two images are selected (one from each class) for testing and the remaining images are used for preparing at every cycle of the cross-validation. Separate midpoints of general accuracy, sensitivity, specificity, and F-measure account for the example dataset after performing the forget-about-one cross-approval method. The process is repeated for the entire dataset to limit irregular impacts of the example, and the entire dataset is used for training and testing. This experiment was separately carried out on Mini-MIAS, DDSM, INbreast, and BCDR datasets, thereby indicating that training and testing dataset samples are selected from a specific database. Table 3 shows the performance analysis of the proposed model using four different datasets. In the literature, it has been discovered that traditional FD and local binary pattern (LBP) features are efficient and strong features used in classification. Therefore, to show the efficacy of the proposed model, traditional FD and LBP are extracted from each dataset and evaluated from the same dataset. The main two reasons behind this step are to show which feature can obtain higher results for mammogram classification as well as observe the efficacy of the proposed M-FD for each dataset. The extracted features are fed to the ANN classifier to classify and discover the cancer subtype as benign or malignant. Furthermore, this section evaluates the proposed model using each database separately, which means that training and testing datasets are taken from the same database. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filters** & **MSE** & **PSNR (db)** & **SNR (db)** \\ \hline HH & 0.026 & 65.99 & 47.62 \\ HH–HL & 0.026 & 65.13 & 47.76 \\ HH–HL–LH & 0.028 & 69.95 & 47.57 \\ HH–LH & 0.27 & 64.32 & 47.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mammogram denoising results based on EWBFEM. A total of 162 benign and 88 malignant samples are selected for training and 44 benign and 22 malignant samples are taken for testing from the Mini-MIAS dataset. Table 3 presents the different results obtained with different features (LBP, FD, and proposed M-FD) for benign versus malignant cases on four datasets. The results showed that the traditional FD outperforms LBP for each dataset of mammogram images. The M-FD model is then proposed to extract features from mammograms for breast cancer classification. The obtained results demonstrated that the proposed M-FD model outperforms the traditional FD and LBP features on the four datasets. The Mini-MIAS dataset obtained an accuracy, sensitivity, specificity, and F-measure of 96.2%, 96.63%, 95.37%, and 97.1%, respectively. A total of 319 benign and 335 malignant samples from the DDSM dataset are used for training whilst 160 benign and 167 malignant samples are used for testing in the first round of evaluation. Samples used in testing were then used for training, and all 981 trained samples from the first round were used for testing the DDSM dataset. The DDSM dataset obtained an accuracy, sensitivity, specificity, and F-measure of 98.57%, 98.33%, 98.8%, and 98.54%, respectively. A total of 127 benign and 73 malignant samples from the INbreast dataset were used for testing whilst 101 benign and 58 malignant samples were used for training in the first round of evaluation. The testing stage used 26 benign and 15 malignant samples. The testing samples were subsequently used for training the remaining samples from the INbreast dataset. This evaluation obtains an accuracy, sensitivity, specificity, and F-measure of 99%, 98.44%, 100%, and 99.21%, respectively. Lastly, this study evaluates 736 additional samples from the BCDR dataset.