Enteroscopy lymphoma auxiliary diagnosis system based on deep learning

Abstract

The invention discloses an enteroscope lymphoma auxiliary diagnosis system based on deep learning. The system comprises an image collection module which is connected to an endoscope host through a collection card, and obtains the information of each frame of image collected by the endoscope host; the training set making module is used for selecting a single-frame image with lymphoma lesions as a training sample, labeling a lesion area in the training sample and generating labeling text information corresponding to a labeling position; the auxiliary diagnosis module is used for constructing anauxiliary diagnosis model and carrying out optimization training on the auxiliary diagnosis model by adopting the training set; and inputting an image to be detected into the trained auxiliary diagnosis model, and outputting an image classification result about whether lymphoma lesions exist or not. According to the method, the lymphoma lesion area under the enteroscope is automatically recognizedbased on the neural network algorithm, a doctor only needs to check the recognized image with lesion again, and the workload of picture check of the doctor is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of auxiliary intelligent diagnosis of lymphoma under enteroscopy, and in particular relates to an auxiliary diagnosis system of lymphoma under enteroscopy based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Small intestinal lymphoma originates from lymphoid follicles under the small intestinal mucosa. Most intestinal lymphomas are a local manifestation of systemic lymphoma. Identifying the characteristics of the small intestinal mucosa through enteroscopy is the most effective method for the diagnosis of small intestinal lymphoma . [0004] The small bowel endoscopy takes a long time, and the endoscopist is very prone to fatigue. At the same time, the endoscopist needs to spend a lot of time viewing the images during the withdrawal process of the small...

AI Technical Summary

Problems solved by technology

[0004] The small bowel endoscopy takes a long time, and the endoscopist is very prone to fatigue. At the same time, the endoscopist needs to spend a lot of time viewing the images during the withdrawal process of the small bowel scope. May cause missed or false detection of lesions due to physical fatigue and visual fatigue

[0005] In addition, due to the insidious onset of gastrointestinal lymphoma, atypical clinical symptoms and various endoscopic manifestations, such as inflammatory polypoid, mucosal erosion, mucosal ulcer, etc., inexperienced endoscopists are very prone to misdiagnosis, which reduces the risk of endoscopic disease. The accuracy of diagnosis by endoscopists leads to a high rate of missed diagnosis and high misdiagnosis rate of lymphoma, so that the time of diagnosis of patients is delayed, which affects the treatment effect and survival time

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