Gastric early cancer auxiliary diagnosis method based on deep learning multi-model fusion technology

A deep learning, auxiliary diagnosis technology, applied in character and pattern recognition, image data processing, instruments, etc., can solve problems such as high risk of missed diagnosis and misdiagnosis, inability to accurately identify lesions and provide diagnosis suggestions, and limited auxiliary role, to avoid problems such as The effect of missed diagnosis

Pending Publication Date: 2020-11-06
WUHAN ENDOANGEL MEDICAL TECH CO LTD
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Problems solved by technology

[0006] Based on the technical problems existing in the background technology, the present invention proposes an auxiliary diagnosis method for early gastric cancer based on deep learning multi-model fusion technology, which can quickly and real-time obtain expert-level diagnostic opinions that meet the diagnostic norms and fit the diagno

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  • Gastric early cancer auxiliary diagnosis method based on deep learning multi-model fusion technology
  • Gastric early cancer auxiliary diagnosis method based on deep learning multi-model fusion technology
  • Gastric early cancer auxiliary diagnosis method based on deep learning multi-model fusion technology

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Embodiment

[0029] S1. The process of constructing multiple models is as follows, including:

[0030] First, build a deep learning image classification model DCNN1 to identify white light and electronic dyeing amplification light source patterns; build a deep learning target detection model DCNN2 to mark and track suspicious lesions under white light; build a deep learning image classification model DCNN3 for white light High and low risk analysis of lesions; build a deep learning instance segmentation model group (DCNNS, composed of DCNN4, DCNN5, and DCNN6), which is used to extract the boundary range, microvascular morphology, and microtissue structure of lesions under staining and magnification; build deep learning decision-making The model DCNN7 is used for lesion property analysis integrating multiple key features. The instance segmentation model group is composed of the deep learning model 4 for extracting boundaries, the deep learning model 5 for extracting microvessels, and the de...

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Abstract

The invention relates to the technical field of medical image processing, in particular to a gastric early cancer auxiliary diagnosis method based on a deep learning multi-model fusion technology, which comprises the following steps: S1, constructing multiple models; s2, collecting gastroscope images, forming continuous serialized image frames, identifying the light source mode of the current image frame by utilizing the image classification model 1, entering the step S3 to mark the position of a focus by the target detection model 2 when the current image frame is identified as a white lightmode, and marking the high-risk focus by utilizing the image classification model 3; and when the image frame is identified as the dyeing amplification mode, entering the step S4 in which a segment model group can extract the boundary range, the microvessel form and the micro-tissue structure feature map in the image frame in real time, and outputting whether canceration occurs or not, the credibility and the differentiation type by the decision-making model 7. A plurality of deep learning models are constructed according to different tasks, a parallel cascade model fusion technology is adopted, and a full-process intelligent auxiliary diagnosis function is provided in the stomach early cancer screening process of endoscopists.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to an auxiliary diagnosis method for early gastric cancer based on deep learning multi-model fusion technology. Background technique [0002] Gastric cancer is a common malignant tumor. According to global cancer statistics in 2018, the incidence and mortality of gastric cancer ranked third, which seriously threatened the quality of life and life safety of patients and caused a huge health burden. The 5-year survival rate of early gastric cancer is over 90%. Therefore, early detection, early diagnosis, and early treatment of gastric cancer are of great significance, which can effectively improve the prognosis of patients, save patients' lives, save happy families, and save medical resources. It has great social value and economic value. [0003] Electronic endoscopy is currently the most widely used and most effective screening method for early gastric cancer in th...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62G06T7/12G06T7/13
CPCG06T7/0012G06T7/12G06T7/13G06T2207/10068G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/30092G06T2207/30096G06T2207/30101G06V2201/031G06F18/24
Inventor 胡孝刘奇为于天成胡珊李超
Owner WUHAN ENDOANGEL MEDICAL TECH CO LTD
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