## 3 Experimental Results and Discussion  
### Experimental Setup  
This study would compare the proposed classifier with the traditional 2D CNN-based classifier, including training time, accuracy, and classifier performances. As seen in Table 2, we also established the two multilayer classifiers by using different numbers of convolutional-pooling layers, different types of convolutional masks, and different sizes of convolutional masks. We adopted a 2D spatial convolutional layer, two convolutional-pooling layers, a flattening layer, and a classification layer [41; 52]. Two \(3\times 3\) fractional-order masks were used to perform the 2D spatial convolutional processes for enhancing the edge information of the possible breast lesions; the fractional-order parameter, \(v\) = 0.30-0.40, provided promising results for feature enhancement (\(v\) = 0.35 was selected in our study [52]). The number of kernel convolutional masks and maximum pooling (MP) masks was set to 16 in 2 nd and 3 rd convolutional-pooling layers, respectively. The sizes of kernel convolutional masks and maximum pooling masks were set at \(3\times 3\) and \(2\times 2\), respectively. Two kernel convolutional-pooling processes were used to extract the desired object's feature \begin{table}
\begin{tabular}{c c c c} \hline
**Classifier** & **Layer Function** & **Manner** & **Feature Pattern** \\ \hline \multirow{6}{*}{Model \#2 Traditional CNN-based Classifiers} & \multirow{2}{*}{1 st Convolutional Layer for Image Enhancement} & \multirow{2}{*}{
\begin{tabular}{c} Fractional-Order-based Convolutional Process \\ (3 \(\times\) 3 Mask, 2, Stride = 1, Padding = 1) \\ \end{tabular} } & \multirow{2}{*}{2D Image (4320 \(\times\) 2600)} \\  & & & \\ \cline{2-3}  & ROI Extraction & ROI Extraction & 2D Image (100 \(\times\) 100) \\ \cline{2-3}  & 2 nd Convolutional Layer for Feature Extraction & 2D Kernel Convolutional Process (3 \(\times\) 3 Mask, 16, Stride = 1, Padding = 1) & \(X_{1}\) (100 \(\times\) 100) \\ \cline{2-3}  & 2 nd Pooling Layer for Feature & Maximum Pooling Process (2 \(\times\) 2 Mask, 16, Stride = 1, Padding = 1) & \(x_{1}\) (50 \(\times\) 50) \\ \cline{2-3}  & 3 rd Convolutional Layer for Feature Extraction & 2D Kernel Convolutional Process (3 \(\times\) 3 Mask, 16, Stride = 1, Padding = 1) & \(X_{2}\) (50 \(\times\) 50) \\ \cline{2-3}  & 3 rd Pooling Layer for Feature & Maximum Pooling Process (2 \(\times\) 2 Mask, 16, Stride = 1, Padding = 1) & \(x_{2}\) (25 \(\times\) 25) \\ \cline{2-3}  & 2 nd Pooling Layer for Feature & Flattening Process & \(x\) (1 \(\times\) 625) \\ \cline{2-3}  & & Back-Propagation Neural Network: 1 Input Layer (625 Nodes), 1 st Hidden Layer (168 Nodes), 2 nd Hidden Layer (64 Nodes), and 1 Output Layer (3 Nodes) & Input Feature Signal (1 \(\times\) 625) \\ \cline{2-3}  & & Learning Algorithm: & \\ \cline{2-3}  & & Gradient Descent Method (ADAM Algorithm) [52; 53] & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 7: Flowchart for breast lesions screening and validation in clinical application with 2D spatial and 1D convolutional neural network-based classifier. pattern and also reduced the dimensions of the feature patterns with a MP process for obtaining abstract features. Each kernel mask moved the number of columns and rows in steps of 1 (stride = 1) at each convolutional operation. The padding parameter was set to 1 to maintain the feature pattern (padding = 1). Each MP mask moved with a stride of 2 (stride = 2). The MP processes could overcome the overfitting problem for training a multi-layer classifier. In the classification layer, for a back-propagation neural network (BPNN) with 1 input layer (625 nodes), 1 st hidden layer (168 nodes), 2 nd hidden layer (64 nodes), and 1 output layer (3 nodes), an adaptive moment estimation method (ADAM) or a back-propagation algorithm was a gradient descent-based optimization algorithm [52; 53] to adjust the BPNN's connecting weighted parameters which was used to determine the optimal parameters to raise the classifier's accuracy.