Gastroenterology model construction method and diagnosis system

Abstract

The invention provides a gastrointestinal disease model construction method and a diagnosis system. The diagnosis system comprises an image acquisition unit, an image labeling unit, an image databaseconstruction unit, an image recognition unit and a data output unit, wherein the image database construction unit comprises a normal image feature library and a focus image feature library, and the image recognition unit comprises a focus type recognition model. Firstly, a normal image feature library of a normal gastroscope image and a plurality of lesion image feature libraries of a lesion gastroscope image are constructed; a focus type identification model is used; color feature and texture feature fusion is carried out on a gastroscope image to be detected, matching and recognition are carried out on the gastroscope image to be detected, a normal image feature library and a focus image feature library in sequence, whether the gastroscope image to be detected contains focuses or not andfocus types are output, and therefore the focuses with various stomach types can be recognized rapidly and accurately.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and relates to a method for constructing a gastrointestinal disease model and a diagnosis system. Background technique [0002] Gastroenterology is a common disease of the digestive system, and its incidence rate is increasing year by year, seriously endangering human physical and mental health. Clinically common gastric diseases include acute gastritis, chronic gastritis, gastric ulcer, duodenal ulcer, gastroduodenal ulcer, gastric polyps, gastric stones, benign and malignant tumors of the stomach, etc. Common types of gastric lesions include gastric mucosal hyperemia, edema, hemorrhage, erosion, ulcer, tumor, stone, polyp, etc. Most gastric lesions can be identified by gastroscopy. However, at present, the identification and diagnosis of gastroscopy images mostly rely on clinicians' own experience to judge suspicious lesion areas, capture the images and save them in the endoscopy rep...

AI Technical Summary

Problems solved by technology

Due to the wide variety of lesions in gastroscopy images, and the possibility of including multiple lesions at the same time, it brings great difficulties to the identification, and the difficulty and accuracy of manual identification are relatively large. Using the commonly used manual identification model requires a large number of training sets , it is difficult to build the model

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