# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
## 3 Experimental Results
### Multilayer CNN-Based Classifier Testing and Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Fold** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score** \\ \hline
1 & 96.60 & 95.70 & 96.80 & 0.9620 \\
2 & 94.80 & 95.60 & 96.00 & 0.9530 \\
3 & 96.10 & 93.00 & 95.40 & 0.9450 \\
4 & 88.30 & 96.20 & 93.00 & 0.9210 \\
5 & 90.10 & 95.30 & 93.60 & 0.9260 \\
6 & 92.10 & 93.40 & 93.80 & 0.9270 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experimental results of K-fold cross-validation (\(K_{f}=10\)) for Model #1–2 with 8 Kernel convolutional windows and 8 maximum pooling windows in each convolutional-pooling layer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Fold** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score** \\ \hline
1 & 95.00 & 96.48 & 95.60 & 0.9574 \\
2 & 94.38 & 95.92 & 95.20 & 0.9514 \\
3 & 94.82 & 91.80 & 93.80 & 0.9389 \\
4 & 95.02 & 96.50 & 95.60 & 0.9575 \\
5 & 96.51 & 91.68 & 95.40 & 0.9577 \\
6 & 94.09 & 94.84 & 94.40 & 0.9447 \\
7 & 92.80 & 97.61 & 95.00 & 0.9515 \\
8 & 92.77 & 95.85 & 94.40 & 0.9429 \\
9 & 96.46 & 95.70 & 96.00 & 0.9608 \\
10 & 95.19 & 95.54 & 95.00 & 0.9536 \\ \hline \hline Average & 95.19 & 95.19 & 95.04 & 0.9516 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of K-fold cross-validation (\(K_{f}=10\)) for the proposed deep-learning-based CNN. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Test** & **2 nd** & **2 nd** & **3 rd** & **3 rd** & **Classification Layer (Fully** \\  & **Convolutional** & **Layer** & **Pooling** & **Convolutional** & **Layer** & **Connecting Network)** & **Average Training Time** \\ \hline
1–1 & & \(3\times 3\), 4 & \(2\times 2\), 4 & \(3\times 3\), 4 & \(2\times 2\), 4 & \(<\)150 \\ \hline
1–2 & & \(3\times 3\), 8 & \(2\times 2\), 8 & \(3\times 3\), 8 & \(2\times 2\), 8 & 168 1 st hidden nodes, & \(<\)240 \\ \hline
1–3 & & \(3\times 3\), 16 & \(2\times 2\), 16 & \(3\times 3\), 16 & \(2\times 2\), 16 & 64 2 nd hidden nodes, & \(<\)280 \\ \hline
1–4 & & \(3\times 3\), 32 & \(2\times 2\), 32 & \(3\times 3\), 32 & \(2\times 2\), 32 & \(<\)2, 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparisons of the training CPU time for multilayer CNN-based classifiers with different numbers of Kernel convolutional windows and maximum pooling windows in second and third convolutional-pooling layers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Fold** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score** \\ \hline
1 & 85.30 & 84.70 & 87.80 & 0.8640 \\
2 & 87.10 & 93.00 & 91.20 & 0.9000 \\
3 & 82.80 & 93.40 & 89.00 & 0.8760 \\
4 & 86.10 & 91.50 & 93.20 & 0.9200 \\
5 & 84.00 & 92.00 & 89.20 & 0.8780 \\
6 & 93.20 & 84.80 & 91.08 & 0.8880 \\
7 & 91.20 & 95.70 & 95.40 & 0.9480 \\
8 & 90.10 & 94.80 & 93.60 & 0.9260 \\
9 & 92.40 & 86.70 & 91.40 & 0.8950 \\
10 & 97.20 & 99.10 & 98.40 & 0.9810 \\ \hline Average & 88.94 & 91.57 & 92.30 & 0.9076 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experimental results of K-fold cross-validation (\(K_{f}=10\)) for Model #1–1 with 4 Kernel convolutional windows and 4 maximum pooling windows in each convolutional-pooling layer. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Fold** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score** \\ \hline
1 & 96.70 & 96.20 & 97.00 & 0.9640 \\
2 & 99.00 & 96.20 & 99.00 & 0.9760 \\
3 & 90.80 & 93.40 & 93.20 & 0.9210 \\
4 & 95.60 & 93.40 & 95.40 & 0.9230 \\
5 & 99.50 & 97.60 & 98.80 & 0.9860 \\
6 & 95.30 & 95.70 & 96.20 & 0.9550 \\
7 & 66.00 & 95.60 & 77.20 & 0.7800 \\
8 & 96.70 & 98.60 & 98.00 & 0.9770 \\
9 & 97.40 & 91.50 & 95.40 & 0.9440 \\
10 & 99.00 & 94.80 & 97.40 & 0.9690 \\ \hline Average & 93.60 & 95.30 & 94.60 & 0.9395 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Experimental results of K-fold cross-validation (\(K_{f}=10\)) for Model #1-4 with 32 Kernel convolutional windows and 32 maximum pooling windows in each convolutional-pooling layer.