Multi-scene digestive tract endoscopic image recognition method and system based on artificial intelligence

Abstract

The invention relates to the technical field of image recognition, and discloses a multi-scene digestive tract endoscopic image recognition method and system based on artificial intelligence. The method comprises the steps of obtaining a data sample of a digestive tract endoscope image, marking a shooting part of the image for the data sample, checking a scene, and marking a segmentation region ofa lesion; training a corresponding shooting part recognition model/inspection scene recognition module based on the data sample set of the same shooting part/inspection scene; aiming at the combination of each shooting part and each examination scene, training to obtain a lesion identification model of the corresponding shooting part in the corresponding examination scene; for a to-be-identifiedendoscopic image, identifying a shooting part and an examination scene through a corresponding identification model, selecting a lesion identification model of the corresponding shooting part in the corresponding examination scene, and identifying a lesion part is identified. According to the scheme, the accuracy of identification and the generalization ability of the system can be improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an artificial intelligence-based multi-scene digestive tract endoscopy image recognition method and system. Background technique [0002] Digestive system tumors account for more than one-third of the incidence of malignant tumors. In actual medical practice, the early symptoms of digestive tract tumors are very inconspicuous. Once symptoms appear, most patients are in the middle and late stages. The most direct and effective method for early detection of gastrointestinal tumors is digestive endoscopy. However, the current development of recognition technology based on digestive endoscopy images still has many shortcomings. For example, the scenes involved in the images of digestive endoscopy are very complex, the parts of the images taken vary greatly, there are many types of lesions in the images, and the characteristics of each lesion Complicated performance. Under...

AI Technical Summary

Problems solved by technology

However, the current development of recognition technology based on digestive endoscopy images still has many shortcomings. For example, the scenes involved in the images of digestive endoscopy are very complex, the parts of the images taken vary greatly, there are many types of lesions in the images, and the characteristics of each lesion Complicated performance

Under such circumstances, the efficiency of traditional manual identification is low, and the missed detection rate and false detection rate are high.

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