# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 2 Literature Review
As prior literature suggests, successfully recognizing colorectal polyps from images primarily relies on fulfilling three major functions: segmentation, localization, and detection. In deep learning, effective image segmentation involves the precise classification of individual pixels and the delineation of boundaries. To achieve the localization function, the coordinates of the bounding box must be calculated correctly. Finally, the detection function can be satisfied by accurately predicting the classification of target objects. Therefore, a deep learning algorithm that aims to attain the three functions, concurrently or separately, must consist of four components: Input, Backbone, Neck, and Head. These components are explained as follows:  
* Input can be an inputted image, a patch, or a processed and sampled image;
* Backbone is responsible for pre-training, and a network based on CNNs such as ResNet, CSPDarkNet, AlexNet, DarkNet, or VGGNet is commonly adopted;
* Neck is to extract features at different levels, and another network such as Feature Pyramid Network (FPN), PANet, or Bi-FPN can be chosen to attain this objective;
* Head is responsible for predicting bounding boxes, and a one-stage network (e.g., Region Proposal Network: RPN, YOLO, or RetinaNet [25]) or a two-stage network (e.g., Faster R-CNN [26] or R-FCN) can be selected for this purpose. Of the current deep learning algorithms, YOLOv4 [27] has gained great popularity among researchers. It is an updated version of YOLOv3 [28]. The older algorithm calls for revision because CNNs can encounter the problem of gradient vanishing when the number of network layers is increased. This leads to information loss at each network layer during the training phase and deteriorates the efficiency of layer learning. For instance, if the information is propagated by copying, as in the case of ResNet, it will demand more computational resources to process. To solve this problem, researchers develop or choose innovative networks as the backbones of YOLOv4. For instance, DarkNet or other DarkNet-based networks such as Cross Stage Partial Network (CSFNet) [29] are commonly selected. With these new backbone architectures, information is split and combined in the propagation process through the use of additional transition layers. This allows certain information to be directly merged with the convolution results, thereby reducing computational complexity, facilitating the network's learning capacity, and increasing the utilization of layer parameters. Accordingly, YOLOv4 achieves higher accuracy but demands lower hardware requirements than the older algorithm while maintaining the same speed. Recently, significant progress has been witnessed in image segmentation following the introduction of CNN-based architectures. By repeatedly downsampling the inputted images, low-level features (also known as feature maps) can be effectively extracted. Subsequently, upsampling can be performed to enable pixel-level prediction and image segmentation. To illustrate, after [PERSON], [PERSON], and [PERSON] [30] had advanced FCN (the first end-to-end trainable image segmentation algorithm), [PERSON], [PERSON], and [PERSON] [31] modified it to create U-Net. U-Net is a deep-learning algorithm specifically designed for medical image segmentation [32]. Its architecture consists of a U-shaped network that enables the capture of both contextual and positional information. It also includes a pathway between an encoder and a decoder (i.e., the skip connection). The encoder comprises multiple convolutional and pooling layers and is responsible for feature extraction. The decoder in U-Net uses deconvolution to restore localization information. With the established skip connections, high-level features learned in the encoder can be transmitted to the decoder. This helps reduce information loss during the upsampling process. Figure 1 illustrates the architecture of U-Net.