## 2 Related Research
### Evaluation of Breast Tumor Detection Performance Using YOLO Detector-Based Preprocessed Data  
In this section, an experiment to measure the accuracy of breast tumor detection was performed. This test checks the proposed LFA algorithm's accuracy in detecting lesion objects. The YOLO model used in this test is YOLOv4. The v4 model learning and verification data using the original image are expressed to increase the test reliability. The datasets include the actual breast ultrasound image database provided by [PERSON], [PERSON] et al. [51] and the database provided by [PERSON] [52]. [PERSON]'s database consists of 830 images, divided into 133 normal images, 487 benign images, and 210 malignant images. [PERSON]'s database consists of 250 images, divided into 100 benign images and 150 malignant images. By combining the two databases, the experiment is conducted with 1080 images (normal: 133; benign: 587; and malignant: 360). The learning dataset of each model used 758, which was 80% of 947 (the number of benign and malignant data), and the test dataset used 322 (the remaining 20% (189) and 133 normal images). Figure 12 shows the results of detecting breast tumor information from breast ultrasound images using the object detection algorithm proposed in this paper. Figure 12a is the accurate measurement result, and (b) shows the loss for each algorithm. The results in Figure 12a show that the proposed algorithm shows the highest accuracy with 94.9%, followed by Yolov4 (89.4%), R-FCN (86.9%), RetinaNet (88.7%), DeepLab v3+ (93.4%), and U-Net (93.2%). It indicates that the brightness equalization and visual pattern standardization of the original image algorithm proposed in this paper  
Figure 12: The accuracy measurement result of the lesion object detection algorithm. shows a close relationship with the lesion object detection result because they generate a new feature map using line segments. In other words, the learning model using the technique proposed in this paper shows high accuracy when the algorithm proposed in this paper show high similarity to the original data. Figure 12b shows the result of the loss measurement. The proposed algorithm showed the lowest loss of 0.067, followed by Yolov4 0.0856, R-FCN 0.219, RetinaNet 0.1974, DeepLab v3+ 0.10320, and U-Net 0.09234. The results confirm that the proposed algorithm shows the best performance in detecting lesion objects. Moreover, the five algorithms used for comparison in the lesion object detection test require setting the best parameters through preliminary tests. On the other hand, the proposed algorithm can be used without a separate parameter setting since it estimates threshold values internally in the lesion object detection process, making it more effective. The channel expansion technique corrects the boundary of the image to be sharper and emphasizes the characteristics of the object to be learned by the YOLO model given the clear lesions in the image. In addition, by removing the noise in the image, the new method could prevent the deterioration of the detection performance, thereby having a better performance than the experiment using the original image. Figure 13 shows the image extracted through the preprocessing of the YOLO detector proposed in this paper. It indicates the mask set provided by the dataset and the lesion location detected through the proposed detection method. The white boxes in Figure 13c mark the detected lesions. The figure confirms that slightly wider areas than the position of the provided mask are detected. The detailed boundaries, becoming visible as the image boundaries, which were not visible because of the low brightness in the original image, become clear through the proposed channel expansion. The lesion imaged detected through this method are processed into reduced data through the dimensionality reduction technique, and then breast cancer is diagnosed through the RNN model. The test evaluated the proposed RNN model's performance by comparing it with CRNN, AlexNet, VGG, and GoogleNet as comparative models.