# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
###### Abstract  
The human digestive system is susceptible to various viruses and bacteria, which can lead to the development of lesions, disorders, and even cancer. According to statistics, colorectal cancer has been a leading cause of death in Taiwan for years. To reduce its mortality rate, clinicians must detect and remove polyps during gastrointestinal (GI) tract examinations. Recently, colonoscopies have been conducted to examine patients' colons. Even so, polyps sometimes remain undetected. To help medical professionals better identify abnormalities, advanced deep learning algorithms that can accurately detect colorectal polyps from images should be developed. Prompted by this proposition, the present study combined U-Net and YOLOv4 to create a two-stage network algorithm called UY-Net. This new algorithm was tested using colonoscopy images from the Kvasir-SEG dataset. Results showed that UY-Net was significantly accurate in detecting polyps. It also outperformed YOLOv4, YOLOv3-spp, Faster R-CNN, and RetinaNet by achieving higher spatial accuracy and overall accuracy of object detection. As the empirical evidence suggests, two-stage network algorithms like UY-Net will be a reliable and promising aid to image detection in healthcare.  
colorectal polyp; colonoscopy; deep learning; image detection; object detection algorithm; two-stage network algorithm +
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benefits patients, medical practitioners, and the healthcare system. Doctors can save as many images as they need for an immediate diagnosis. These digitalized images can also be scrutinized for further justification and assessment. Moreover, such images can be applied to train computer-aided diagnosis (CADx) algorithms (e.g., image detection algorithms) to assist medical professionals in making correct diagnoses [6]. For instance, busy and exhausted protologists, particularly those working in understaffed medical institutions, can employ CADx tools to help detect abnormalities from images and reduce the miss rate for colorectal polyps.  
Only a limited number of image detection algorithms have been specifically designed to analyze medical images. One such algorithm is U-Net, which can extract information from a large number of images. Another algorithm that excels over others in detection accuracy is YOLOv4. However, no study has yet integrated the two algorithms to test whether the resulting model could accurately detect colorectal polyps from colonoscopy images. To bridge this research gap, the present study first combined U-Net and YOLOv4 to create a two-stage, deep-learning network algorithm called UY-Net. Its accuracy was then evaluated. Based on the evidence collected through this study, two contributions are noted:  
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2. UY-Net displays higher spatial accuracy and overall accuracy of polyp detection than other single detection algorithms such as YOLO3-spp, YOLOv4, RetinaNet, and Faster R-CNN. The development and utilization of the two-stage deep learning network, instead of the one-stage or two-stage detection algorithms, can significantly reduce misdiagnosis for colorectal polyps. Patients only receive the most suitable treatment when colorectal polyps are accurately detected. UY-Net can support clinicians in accomplishing this goal.
3. For the two-stage network algorithm, the sequence of performing image segmentation followed by image detection assumes a critical role in enhancing its accuracy. Precise segmentation of objects in advance can improve the performance of subsequent detection algorithms. This accounts for why the detection accuracy of UY-Net reaches a significantly high level.