Cancer lesion range prediction supporting system based on deep learning and method

Abstract

The invention discloses a cancer lesion range prediction supporting system based on deep learning and method. The system comprises a client and a server, wherein the client is used for monitoring an image acquired by the current endoscope device and transmitting the image to the server, and an analysis result fed back by the server is received and displayed; and the server is used for instantly judging the part corresponding to the image and the part features according to the image acquired by the client, and the analysis result is fed back to the client. When the endoscope device performs image acquisition and the client is triggered, the collected endoscope image is acquired and transmitted to the server; the server receives the endoscope image as a parameter, a convolutional neural network model is called for analysis on whether the endoscope image is qualified and analysis on part judgment and part feature recognition; and according to the acquired analysis result, the client callsan image representing each part and a sign representing the part feature for overlapped display. An early cancer can be diagnosed, a cancerization range is circled, the operation is simple and easy to use, and social and economic value is significant.

Description

technical field [0001] The invention belongs to the technical field of medical detection, and in particular relates to an auxiliary system and method for predicting cancer lesion range based on deep learning. Background technique [0002] With the enhancement of people's health awareness and the gradual increase of digestive endoscopy, the detection of digestive tract submucosal tumors, early cancers and precancerous lesions has increased rapidly. Traditional surgery has been gradually replaced by endoscopic interventional therapy due to the large surgical trauma, slow recovery of patients, and greatly reduced quality of life after surgery. For endoscopic resection of cancer lesions, it is critical to determine the extent of resection. If the resection range is too large, although the cancerous part can be removed, it will have a certain impact on the patient's quality of life; if the resection range is too small, the lesion will not be completely removed, which will lead t...

AI Technical Summary

Problems solved by technology

If the resection range is too large, although the cancerous part can be completely removed, it will have a certain impact on the patient's quality of life; if the resection range is too small, the lesion will not be completely resected, which will lead to recurrence and seriously endanger the patient's life

In fact, it is still difficult to accurately locate the disease, and it is difficult to identify with the naked eye if it is a mild cancer

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