## 2 Related Research
### Prediction Deep Learning-Based Studies on Medical Image Lesion Detection and Classification  
Deep learning-based image analysis is a learning model centered on convolution operation. The leading models include AlexNet, U-Net, DarkNet, ResNet, and VGG. Among them, AlexNet, U-Net, and DarkNet are the most widely used, and additional learning models are sometimes linked to these models. [PERSON], [PERSON], et al. [40] proposed reducing the computational requirement and improving accuracy using CAD and an AlexNet-based diagnostic model. This method performed basic preprocessing, scaled the data in four ways, and used it as an input for the AlexNet model. It resulted in 95% accuracy, 97% sensitivity, 90% specificity, and 99.36% AUC. [PERSON], [PERSON] et al. [41] conducted a study on the diagnosis of lymph node metastasis in breast cancer patients to improve the performance of an automated CAD system in digital histopathology. By individually training four histopathology data through U-net, it was able to show higher performance than conventional U-net learning. It resulted in an 87-89.5% AUC, 82.0% sensitivity, and 87.8% specificity. Since U-net shows very effective performance in detecting an object and uses a relatively small amount of data, it can effectively detect objects from data such as medical images that are difficult to collect data. [PERSON], [PERSON] et al. [42] proposed a pipeline that can effectively segment skin lesions by combining the YOLO and GrabCut algorithms. This method detected lesions in four steps. Step 1 removed hair around the lesion and Step 2 detected the lesion location through YOLOv3. Step 3 combines the lesion image and the detected location, and Step 4 creates a binary image according to the lesion shape through the GrabCut algorithm. The method showed excellent performance of 90.82% sensitivity, 93.39% accuracy, and 92.68% specificity. However, the AlexNet model requires a large volume of data at the level of big data, and each data point must be labeled and managed. Moreover, it has a disadvantage in that it is difficult to maintain the spatial and structural consistency of the image segmentation result [43; 44]. While U-net mainly performs the convolution operation in the contracting path, bottleneck path, and expanding path, the feature map is larger than conventional models because it does not perform padding. Therefore, it causes the more computational problem of processing a large volume at the learning and detection steps. Since the number of channels to be created is doubled according to the layer configured in the contracting path, the computational requirement is higher than that of the reference model. Although the overall processing speed is fast, the system resources (PC specification, energy, etc.) required by each processing are high [45; 46; 47]. In the case of DarkNet, its performance in detecting and classifying lesion objects in medical images is better than other technologies. However, the processing speed is lowered due to the large volume of input image data, and accurate analysis is difficult due to limited resource use, potentially leading to a disabled processing state. Resolving it requires converting the high-quality original images into low-dimensional data to enable accurate analysis by reducing the processing speed according to data processing and high resolution [48; 49]. This study used YOLO, a DarkNet model with high performance in object detection and classification, and developed pre-processing to increase the detection performance of the YOLO model, a dimensionality reduction technique, and a pipeline for the RNN model. Breast Cancer Diagnosis Model Based on Image Channel Expansion and Visual Pattern Standardization Algorithm  
Breast ultrasound images are data generated by radiating an ultrasonic signal and converting a reflected and projected signal into a brightness value. It shows strong noise with respect to brightness.