Image processing method and system based on convolutional neural network

A convolutional neural network and image processing technology, applied in the field of image processing methods and systems based on convolutional neural networks, can solve problems such as poor robustness

Pending Publication Date: 2021-05-28
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

The above methods are all unsupervised methods, which only extract a certain

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  • Image processing method and system based on convolutional neural network
  • Image processing method and system based on convolutional neural network
  • Image processing method and system based on convolutional neural network

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Embodiment Construction

[0039] The present invention will be further described in detail below in conjunction with the examples.

[0040] An image processing system based on a convolutional neural network, including: a preprocessing module, a segmentation module and a ResNet50-II convolutional neural network module.

[0041] The preprocessing module is used to preprocess the MRI three-dimensional images in the data set to obtain two-dimensional images;

[0042] The segmentation module is used to segment the preprocessed two-dimensional image, and obtain the image of the region of interest after the segmentation;

[0043] The ResNet50-II convolutional neural network module is used to divide the segmented image containing the region of interest into a training set and a test set, and input the images in the training set to the ResNet50-II convolutional neural network for training; after the training is completed, the The images of the test set are input into the ResNet50-II neural network for classifica...

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Abstract

The invention discloses an image processing method and system based on a convolutional neural network, and the method comprises the steps: carrying out the preprocessing of a three-dimensional brain image in a data set, and obtaining a two-dimensional image; segmenting the preprocessed two-dimensional image by adopting a stack segmentation algorithm, and obtaining an image of the region of interest after segmentation; constructing a ResNet50-II convolutional neural network, dividing the segmented image into a training set and a test set, and inputting the image in the training set into the ResNet50-II convolutional neural network for training; and after the training is completed, inputting the images of the test set into the ResNet50-II neural network for classification. According to the method and system, the classification accuracy is greatly improved.

Description

technical field [0001] The present invention relates to an image processing method and system, in particular to an image processing method and system based on a convolutional neural network. Background technique [0002] Medical imaging has various image modes, such as magnetic resonance imaging (Magnetic Resonance Imaging, MRI for short), x-ray, CT, ultrasound imaging and the like. Magnetic resonance imaging (MRI) is currently the most widely used technique in the field of radiography. As a dynamic and flexible technique, MRI can achieve variable image contrast and output images of different channels. Moreover, the soft tissue resolution of magnetic resonance imaging is several times that of CT, and it can sensitively detect changes in water content in tissue components, which makes it excellent in organs with numerous tissues such as the brain. Compared with CT, it can usually More efficient and earlier detection of lesions. In terms of the effect of auxiliary diagnosis...

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Application Information

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IPC IPC(8): G06T7/11G06K9/62G06N3/04G06N3/08
CPCG06T7/11G06N3/04G06N3/08G06T2207/10088G06T2207/30016G06T2207/30096G06F18/241G06F18/214Y02T10/40
Inventor 夏景明邢露萍谈玲
Owner NANJING UNIV OF INFORMATION SCI & TECH
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