# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
## 2 Materials and Methods  
### Design of the Multilayer Deep-Learning-Based CNN  
Multilayer deep-learning-based CNN includes the multiple convolutional layers, pooling layers, and pattern recognition layers (classification layers). It is a multilayer model for image classification that combines multiple functions, such as image feature enhancement, feature extraction, parameter simplification, and pattern recognition. In recent years, deep-learning and broad-learning technologies have been applied for medical image processing, segmentation, and classification [3; 4; 5; 6; 7; 8; 9; 13; 14; 15]. Compared with traditional MLP networks, CNN can process feature enhancement and extraction at the front end of the network; therein lies its advantage in processing 2D medical images. However, image processing generates a considerable number of parameters, increasing the amount of computations and the time required for computing. With pooling processes, the number of features is reduced in the middle of the network to reduce the computational time. Finally, the image classification layer assists with screening tasks using a pattern to separate the normality from abnormalities. This study uses the multilayer CNN model to develop an automatic breast tumor screening based on 2D mammogram images, as shown in Figure 1. This automatic screening process includes: (1) Region of interest (ROI) extraction; (2) feature enhancement and extraction; and (3) rapid screening of breast tumors. Each function is described as follows:  
ROI extraction: In this study, we use digital data in the mammogram X-ray image database (161 female patients, 322 images) [33; 34] provided by the MIAS. The biomarkers of MIAS database clearly mark the positions and tumor sizes [33]. The statistical results of probability distribution of tumor locations, in accordance with the MIAS database's biomarkers, are shown in Figure 2a. Looking at the distribution probability of tumor locations, the most frequent location of tumors is the right and the upper outer quadrant of the breast. Given a 2D image with 4320 pixels \(\times\) 2600 pixels, we define the priority for automatically extracting ROI with a specific bounding box based on the distribution probability. Areas with greater probability are first in line for ROI extraction, and the priority order is stored in the work queue. The priority order is shown in Figure 2b. ROI image extraction and tumor detection is performed as per the priority order. * Feature enhancement and extraction: A multilayer 2D convolution operation is used to magnify the texture of what might be tumor tissue and edge information (usually two or more layers are used), as shown in Figure 2a. Each layer uses a 3 \(\times\) 3 sliding window to perform the operation of the convolutional weight. First, a 2D fractional  
Figure 2: ROI block cutting and priority extraction. (**a**) The statistics of the prevalence of malignant and benign tumors; (**b**) priority of ROI block for automatic ROI extraction. convolution operation is performed to magnify the tumor characteristics. Then, by combining multilayer convolutional weight calculations, the contour of the tumor is gradually strengthened, noise is removed, and the image is sharpened. These effects help strengthen the target area and retain non-characteristic information.