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

Abstract

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.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and relates to a medical endoscope image recognition system and method, in particular to a deep learning-based auxiliary diagnosis system and method for ulcerative colitis under colonoscopy. Background technique [0002] With the continuous development and maturity of deep learning algorithms, it has been gradually used in the field of medical image analysis. Endoscopic images are an important basis for doctors to analyze patients' digestive tract diseases. In recent years, a variety of methods for screening and diagnosing lesions using deep convolutional neural network models have been developed, which is of great clinical significance in the current colonoscopy diagnosis system. [0003] Ulcerative colitis is a continuous inflammation of the colonic mucosa and submucosa. Its colonoscopy shows continuous and diffuse distribution. The surface of the mucosa is congested and eroded, mostl...

AI Technical Summary

Problems solved by technology

[0005] The present invention mainly solves the problem that the traditional colonoscopy report system relies on manual image collection, which is prone to missing blind spots and suspicious lesion areas in site inspection. It uses image recognition technology to monitor endoscopic video in real time, and automatically collects key organ sites and suspicious lesion areas. After the images are selected globally according to the weighted algorithm, they are saved in the colonoscopy report system

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