# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 4 Results and Discussion  
The values of AP and IoU were computed and used as indexes to estimate the accuracy of object detection. Table 2 presents these results. The table shows that the AP and IoU values for YOLOv4 and YOLOv3-spp are all above 0.81, indicating that the two YOLO models detect polyps to an adequate level. This finding aligns with what previous research has reported [34]. For instance, [PERSON] et al. [35] tested five YOLO algorithms (i.e., YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, and YOLOv4-tiny with the Inception-ResNet-A block), and all models were found to achieve at least 89% training accuracy and 85% testing accuracy. In other words, they effectively detected polyps by drawing bounding boxes around these detected objects. As [PERSON] et al. 's study and the present study suggest, YOLO algorithms can aid medical practitioners in detecting abnormalities from endoscopic images. However, UY-Net achieves a significantly higher accuracy level (AP = 0.9915; IoU = 0.9395), exceeding that of YOLOv3-spp or YOLOv4 by at least 10%. Based on this finding, the first hypothesis of this study can be substantially corroborated:  
1. Applying U-Net followed by YOLOv4 results in considerably higher accuracy in detecting colorectal polyps from colonoscopy images. The proposed two-stage network displays the highest levels of spatial accuracy and overall accuracy of object detection. This suggests that image detection should not be carried out alone but coupled with image segmentation. For example, [PERSON] et al. [36] proposed a two-stage design that used transformers to detect polyps in colonoscopy images. In the segmentation stage, they first used the Dense Prediction Transformer (DPT) model \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Learning Rate** & **Optimizer** & **Batch Size** & **Anchors** & **Loss** & **Threshold** \\ \hline Faster R-CNN & \(2.5\times 10^{-4}\) & Adam & 8 & 256 & L1s\({}^{\text{smooth}}\) log loss & 0.4 \\ RetinaNet & \(1\times 10^{-5}\) & SGD & 8 & 15 & L1s\({}^{\text{smooth}}\) focal loss & 0.3 \\ YOLOv3-spp & \(1\times 10^{-3}\) & SGD & 16 & 8 & MSE, CE & 0.25 \\ YOLOv4 & \(1\times 10^{-3}\) & SGD & 16 & 8 & CioU, CE & 0.25 \\ UY-Net & \(1\times 10^{-3}\) & SGD & 32 & 18 & CioU, CE & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyper-parameters of YOLOv4 and UY-Net. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Backbone** & **AP** & **IoU** \\ \hline Faster R-CNN & ResNet & 0.7866 & 0.5621 \\ RetinaNet & ResNet & 0.8697 & 0.7313 \\ YOLOv3-spp & ResNet & 0.8105 & 0.8248 \\ YOLOv4 & DarkNet & 0.8513 & 0.8205 \\ UY-Net & DarkNet, U-Net & 0.9915 & 0.9395 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AP and IoU for different algorithms. to extract depth maps of salient objects. Then, they used the Visual Saliency Transformer (VST) architecture to extract depth geometric information of regions associated with these suspicious objects. In the second stage, DEtection TRansformer (DETR) architecture was applied to detect the polyps. [PERSON] et al. 's model achieved an AP of 0.92 in the Kvasir-SEG dataset. Like UY-Net, it can also accurately detect colorectal polyps in medical images. Therefore, a design with two stages, first for image segmentation and/or extraction, followed by image detection, may be a promising framework for facilitating polyp detection accuracy. Moreover, UY-Net surpasses RetinaNet by at least 12% and Faster R-CNN by 20% in accuracy. RetinaNet is a detector that combines region proposal generation and object classification into one stage. By simplifying its architecture and incorporating FPN to create a feature pyramid, RetinaNet may perform well on detection accuracy and speed [25]. Faster R-CNN [37], on the other hand, is a two-stage detection algorithm. In its first stage, RPN is used to output a set of regional proposals. In the second stage, these regional proposals are used for object detection and classification.