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Training method of Faster RCNN (Recurrent Convolutional Neural Network) for enhancing automatic identification of CT (Computed Tomography) image of gastric cancer

An automatic recognition, CT image technology, applied in the field of image recognition, to achieve high accuracy

Pending Publication Date: 2020-01-31
QINGDAO UNIV
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Problems solved by technology

[0007] The present invention proposes a Faster RCNN network training method for automatic identification of enhanced CT images of gastric cancer, which solves the problem in the prior art of manually predicting the depth of tumor cell infiltration into the gastric wall based on enhanced CT images of gastric cancer

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  • Training method of Faster RCNN (Recurrent Convolutional Neural Network) for enhancing automatic identification of CT (Computed Tomography) image of gastric cancer
  • Training method of Faster RCNN (Recurrent Convolutional Neural Network) for enhancing automatic identification of CT (Computed Tomography) image of gastric cancer
  • Training method of Faster RCNN (Recurrent Convolutional Neural Network) for enhancing automatic identification of CT (Computed Tomography) image of gastric cancer

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Embodiment Construction

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0041] The present invention proposes a Faster RCNN network training method for automatic recognition of enhanced CT images of gastric cancer, constructs a training set to train the Faster RCNN network, and tests the training effect of the training set through a test set.

[0042] like figure 1 Shown, the training method of the Faster RCNN network that is used for gastric cancer enhanced CT image automatic recognition of the present invention comprises the fol...

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Abstract

The invention provides a training method of a Faster RCNN (Recurrent Convolutional Neural Network) for enhancing automatic identification of a CT (Computed Tomography) image of gastric cancer. The training method comprises the following steps: acquiring a gastric cancer image in a progress stage; manually identifying the image; extracting a region of interest on the image by using a Faster RCNN network; preprocessing the images in the data set; performing standardization processing on the preprocessed image; dividing the standardized images into a training set and a test set; inputting the training set images into a network; verifying the training set through the test set; when the prediction effectiveness of the training set reaches a preset value, ending the training; and when the prediction effectiveness of the training set is lower than a preset value, reconstructing the training set for training. The Faster RCNN network trained by the method of the invention can identify the gastric cancer tumor in the progress stage of the enhanced CT image, and can perform T-stage processing on the gastric cancer tumor in the progress stage.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a training method for a Faster RCNN network used for automatic recognition of enhanced CT images of gastric cancer. Background technique [0002] Gastric cancer currently ranks fifth in cancer incidence and third in mortality worldwide, and has become the third biggest killer threatening the health of people in the world. Accurate preoperative gastric cancer staging is crucial for the selection of treatment plan and the prediction of postoperative curative effect of patients. [0003] At present, the examinations applied to the preoperative staging of gastric cancer include endoscopic ultrasonography (EUS), multi-detector computed tomography (CT), magnetic resonance imaging (MRI) and combined positron emission tomography (PET-CT). examine. MRI is not a routine examination for gastric cancer because of its high requirements for examiners and the limitation of long-term...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/73A61B6/00G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06T7/73A61B6/5217A61B6/58G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30092G06T2207/30096G06N3/045
Inventor 卢云吴庆尧孙品
Owner QINGDAO UNIV
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