###### Abstract
The deep CNN-based methods combine the multiconvolutional pooling layers (>10 layers in general configuration) and a classification layer to perform the automatic end-to-end enhancement process, noise filtering, feature extraction, and pattern recognition in this proposed topic, such as amass classification, lesion detection and localization, and lesion segmentation/ROI detection, by using fully convolutional network (FCN), Unet CNN, region-based CNN (R-CNN), faster R-CNN, TTCNN (transferable texture convolutional neural network), and Grad-CAM (gradient-weighted class activation mapping)-based CNN [31; 32; 33; 34; 35; 36; 37; 38]. The multiconvolutional-pooling processes can extract the desired features from low-level features to high-level information (sharpening process) for detecting nor \begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Assessment** & **Follow-Up** & **Risk Factor (\%)** \\ \hline
0 & Inconclusive result & Requires additional imaging evaluation & - \\ \hline
1 & Normal (Negative) & No lesion found and requires routine screening & 0 \\ \hline
2 & Benign finding & No malignant lesion and requires routine screening & 0 \\ \hline
3 & Probably benign finding & Requires short interval or continued screening & \textless{}2 \\ \hline
4 & Suspicious finding & Requires tissue diagnosis & 3–94 \\ \hline
5 & High probability of malignancy & Requires tissue diagnosis & \textgreater{}95 \\ \hline
6 & Proven malignancy & Requires surgical excision & 100 \\ \hline \end{tabular}
\end{table} 
Table 1: Assessment of BI-RADS categories for mammogram classification [11; 16; 17]. mality objects, and then can increase the detection accuracy. However, these configurations will result in increasing the complexity level and computational time and increases the data dimensionality and the volume of training datasets for leading to address overfitting problems during the training of a classifier. Moreover, when classifying the mammography dataset, maximum pooling (MP) is usually used because of the near background for normality, and we are interested in the lighter region for abnormality extraction. The MP will select the brighter pixel values from the image in a specific pooling mask; hence, the dimension of the feature patterns can be effectively reduced, thereby overcoming the overfitting problems in training tasks with too much training datasets [39, 40, 41]. The ROI is proved using the Grad-CAM, which also replaces the conventional lighting with a fully linked layer that uses global average pooling (GAP) [38]. The feature patterns are then obtained by activating the rectified linear unit (ReLU), utilizing the summation and multiplication of feature patterns, respectively, using the GAP. [42]. Therefore, the classification accuracy of these multi-layer structures can be improved for digital image classification. However, the limitations of the multilayer classifier are determining the number of convolutional-pooling layers, the number of convolution kernels, and the sizes of convolutional masks for setting the structure of convolutional-pooling layers. Moreover, too many multiconvolutional-pooling processes will result in spatial and edge information loss, and have no use for the key feature extraction. Hence, we intend to design a 2D spatial and 1D CNN-based classifier to simplify the tasks of image enhancement, feature extraction, and pattern recognition, comprising a two-dimensional (2D) spatial convolutional layer, a flattening process layer, one-dimensional (1D) convolutional layers, a pooling process layer, and a classification layer, which are integrated into an individual multilayer classifier for breast lesions screening. In the 2D spatial convolutional layer, fractional-order-based convolutional, Grad-CAM activation mapping, integral image (II) operations [41, 42, 43, 44, 45] can be employed to perform the convolutional processes to detect the desired object's edge and contour in the specific region along the horizontal and vertical directions.