# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 2 Literature Review  
Most of the early algorithms used for detecting colorectal polyps involved analyzing edge shapes [7], textures [8], colors [9], or a combination of these factors [10]. For example, [PERSON] et al. [7], who had observed that most polyps have an elliptical shape, proposed a new model to detect colorectal polyps. They applied the marker-controlled watershed algorithm, along with other techniques, to conduct region segmentation, ellipse fitting, and ellipse filtering by computing curve direction, curvature, edge distance, and intensity. In contrast, [PERSON] et al. [8] chose texture features, like grayscale intensity and local binary patterns, to distinguish colorectal polyps. [PERSON], [PERSON], and [PERSON] [10] employed shapes and texture features to recognize polyps. They differentiated regions with polyps from polyp-free areas by analyzing texture features such as local binary patterns (LBP: a texture descriptor used to represent the local texture of a computer image or vision by comparing the intensity of a pixel to those of its neighboring pixels), distribution of intensity values, and frequency content of a local neighborhood. They also examined shape features by considering boundary curves to enhance the reliability of localization. However, the applicability of these traditional models is limited and restricted because they can only recognize typical polyps but not those with non-typical shapes or textures. With the recent development of deep learning techniques, algorithms based on Convolutional Neural Networks (CNNs) have gained considerable attention. Take [PERSON] et al. 's study [11] as an example. They used WM-DOVA energy maps to localize the positions of colorectal polyps without considering the sizes or types of such polyps. [PERSON], [PERSON], and [PERSON] [12] advanced a fully automated system to segment colorectal polyps using a Fully Convolutional Network (FCN) for pixel-level prediction. Likewise, [PERSON] et al. [13] adopted CNNs and achieved state-of-the-art (SOTA) performance in a competition to detect colorectal polyps in colonoscopy videos automatically. [PERSON] et al. [14] also employed a region-based CNN for the automated detection of colorectal polyps in colonoscopy. They chose Inception ResNet for feature learning and incorporated post-processing techniques to reach more reliable detection. Another study by [PERSON] et al. [15] used Generative Adversarial Networks (GAN) [16] to generate images of colorectal polyps. In their study, image generation was unsatisfactory, but image detection was still significantly improved. Moreover, a study by [PERSON] et al. [17] showed that using the SegNet architecture [18] to detect colorectal polyps achieved a detection speed of 25 frames per second. It also demonstrated high sensitivity, specificity, and memory efficiency. [PERSON] et al. [19] selected GAN to segment colorectal polyps from images. In their model, GAN comprised the generator and discriminator. The generator was responsible for generating polyp segmentation masks, while the discriminator distinguished real masks from fake ones. Since the generator and discriminator were incorporated, high segmentation precision was observed on a challenging dataset. Similarly, [PERSON] and [PERSON] adopted the Fully Convolutional Neural Network (FCNN) architecture, reporting that their proposed algorithm effectively segmented polyps from images [20; 21]. Along with these researchers, [PERSON] and [PERSON] [22] trained and fine-tuned two Mask R-CNN models where ResNet50 and ResNet100 were used as backbone architectures, respectively. By combining the two models using an ensemble method, their resulting framework significantly outperformed other SOTA methods in segmenting colorectal polyps. [PERSON] et al. [23] utilized the YOLOv2 algorithm [24] for the localization and detection of colorectal polyps. They contended that YOLOv2 yielded high sensitivity and near real-time computational performance, with great potential to compensate for the limited visual field of an endoscopist.