An auxiliary diagnosis system and method for ulcerative colitis under enteroscopy based on deep learning

The invention discloses an auxiliary diagnosis system and method for ulcerative colitis under enteroscopy based on deep learning. The system comprises an enteroscopy image automatic acquisition subsystem, a client, a server and a database. The enteroscope image automatic acquisition subsystem is used for acquiring an enteroscope image; The client is used for uploading the enteroscopy image acquired by the enteroscopy image automatic acquisition subsystem to the server, and judging whether the image is qualified or not and whether the image comprises ulcerative colitis judgment or not, and an analysis result fed back by the server is received and displayed; the database is used for storing the sample set for training the convolutional neural network, the acquired enteroscopy image and the analyzed and output information. According to the method, an image recognition technology is utilized to monitor an endoscope video in real time, images containing key organ parts and suspicious focusareas are automatically collected, a neural network model is utilized to automatically screen the images, the most valuable image can be extracted from a global video, and more reliable and efficientsupport is provided for doctors to diagnose.