# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 5 Conclusions  
The novelty of the present study lies in the incorporation of a segmentation algorithm and a detection algorithm into a two-stage network. While some researchers continue to focus on improving single algorithms, the present study explores a novel and promising alternative by developing and evaluating the two-stage model for polyp detection. The present study also takes an innovative approach to model training. The segmentation algorithm is trained first, followed by the detection algorithm. The sequence of training may assume a significant role in the high accuracy of the proposed model. Taken together, the colorectal polyps in colonoscopy images can be computationally quantifiable and identifiable through object localization and detection after being precisely segmented. UY-Net, therefore, can outperform any of the single detection algorithms in the accuracy of colorectal polyp detection. This sheds light on the potential of a two-stage network model for improving the detection and diagnosis of abnormalities in medical images.  
It is important to note that the present study deliberately lowers resolutions of certain original input images (e.g., from 1920 \(\times\) 1072 to 512 \(\times\) 512) to reduce memory complexity and time required to run the algorithms. Additionally, all the algorithms are executed on a GPU instead of a CPU. This would improve runtime. Moreover, U-Net is trained with the Adam optimizer in order to reduce memory usage and increase inference speed. However, UY-Net contains YOLOv4, a deep-learning algorithm known to be memory-intensive. It also needs to run U-Net. Accordingly, UY-Net excels at polyp detection but incurs greater memory complexity and longer runtime when contrasted with a single algorithm. Researchers, therefore, should continue to delve deeper into the use of deep learning in bioengineering to develop fast, reliable, and efficient algorithms for image detection.  
Although the findings are promising, caution should be exercised before the results can be appropriately generalized. First, colorectal polyps may develop into cancer, so failing to detect them will pose a life-threatening danger to patients. Researchers still need to improve the UY-Net algorithm to reduce the miss rates. To this end, we plan to replicate the present study and incorporate the relatively recent U-Net3+ and YOLOv7 into a two-stage model. We will then compare the accuracy of this new model with that of UY-Net to assess if it can better detect colorectal polyps than the older model. Second, calculating the bounding box based on the edges seems intuitively simple. Nevertheless, the edges obtained by U-Net tend to be less smooth. These minor irregularities in the edges may or may not impact the precision of image segmentation. More effort is needed to clarify this concern.  
Conceptualization, C.-S.H. and Y.-C.L.; Software, J.-W.W.; Validation, C.-J.W.; Writing--original draft, J.-W.W.; Writing--review and editing, C.-S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.