## 2 Related Research
### Visual Pattern Standardization Using Dimensionality Reduction
#### 3.2.2 Dimensionality Reduction through Normalization of Visual Information Using Detection Filter  
The binary contour detection was performed through noise removal by the ROI of only the area where the lesion was detected in the breast ultrasound image. The calculated binary contour image expresses the lesion shape with a white line. For dimensionality reduction, the type and number of line segments required to express the shape of a specific lesion are aggregated by cumulatively counting information showing the same pattern by normalizing the shape of these lesions into a series of numerical patterns. Figure 11 shows the process of converting visual information into a series of numeric patterns. Figure 11 shows the partition of the lesion image into the size of \(N\times N\) and 16 ordered small pieces of images. This order becomes the reference for sorting detailed data to calculate the final result. The image sequence is arranged from the outermost to the inner region of the image. It utilizes the fact that human eyes recognize an internal object by recognizing its external (outer) shape. Figure 11b shows the process of converting visual information into a series of numeric patterns for normalization using a detection filter. The detection filter is a filter with the value of \(2^{N}\) as a coefficient, and the total sum cannot exceed 15. This method obtains a numeric pattern corresponding to the actual pixel count of the image, and when these are summed, different values can be calculated for each visual information. This method applies the engineering method of converting binary numbers to decimal numbers, and the calculated values have the values of \(0\leq x\leq 15\). Table 1 shows the conversion using a detection filter. Table 1 shows the types and unique numbers of visual information according to the response coefficient of the filter. We used these filters to scan all 16 partitioned images and create a one-dimensional array for each image. This array creates a 16-size 1-D array by counting the visual information expressed in each image using the unique numbers in Table 1. The process creates a small matrix that scales down an image sized \(N\times M\) into an image sized \(16\times 16\). Each row of this matrix is the same as the sequence of the partitioned images in Figure 11. The data reduced in the process is used as an input for the RNN model. Breast Cancer Diagnosis Model Based on Image Channel Expansion and Visual Pattern Standardization Algorithm  
This section designs an RNN-based learning model using the images generated through the above process and performs performance evaluation. Table 2 shows the RNN structure proposed in this study. The structure of the RNN model used in this paper to identify the types of breast tumors is composed of five layers. The input layer is a time series \(N\times N\) matrix processed through dimensionality reduction that normalizes the visual information of the image into a series of patterns. Such normalized data are classified through two recursive layers, LSTM and Bidirectional-LSTM. The number of nodes in each of the two recursive layers is 512. The reason for using the recursive layer in this paper is that the normalized matrix has a fixed image sequence, as shown in Figure 11 above. The first row corresponds to the outermost part of the partitioned image, and as it moves down, it moves to the inner region of the image. It is because when the human eyes carefully observe an object for the first time, the eyes unconsciously examine the appearance of the object first and then