LAUSR

Wireless capsule endoscope (WCE) has been verified in clinical medicine for many years. However, the detection process needs experienced doctors to read the film manually for a long time. In addition, the cost of the endoscope itself leads to a high cost of WCE detection and overall cycle is long. New research method based on deep learning technology with robustness and high accuracy can reduce the detection cost and benefit the public. According to the characteristics of small intestine lesion, a method focuses on labeling and feature detection which can optimize the process by analyzing small intestine WCE image and experimental comparison. Based on the YOLOv3 detection network, retaining the original basic feature of detection network, an improved one is further optimized and effectively verified. Finally, the redundant images are filtered out by comparing the Hash value of images, presenting the final concise detection results for doctors. Starting from image labeling, the design of deep learning network structure for the image of small intestine digestive tract endoscope is studied, which can effectively improve intelligent detection computer-aided clinical application of WCE, with higher accuracy and lower missing detection rate than manual detection.