Lung tumor recognition system and method based on convolutional neural network

A convolutional network, lung technology, applied in the field of lung tumor recognition system based on convolutional neural network, can solve the problem of low recognition accuracy, and achieve the effect of improving the recognition rate, improving the recognition accuracy, and reducing the amount of calculation.

Active Publication Date: 2016-12-07
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a lung tumor identification system and me

Method used

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  • Lung tumor recognition system and method based on convolutional neural network
  • Lung tumor recognition system and method based on convolutional neural network
  • Lung tumor recognition system and method based on convolutional neural network

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Experimental program
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Embodiment 1

[0040] Such as figure 1 As shown, the present invention provides a schematic structural diagram of an image heat prediction system. The image heat prediction system includes software and hardware installed in computer equipment. Wherein, the hardware in the computer device includes: an input unit, a processing unit, a storage unit, a cache, and a display unit, etc., wherein the processing unit may include a chip or an integrated circuit dedicated to a convolutional neural network and include A computer program for the convolutional neural network algorithm. The processing unit allocates the operation of each hardware through the time sequence set by the program, so as to execute the functions of the following devices. Wherein, the computer equipment includes, but is not limited to: a single server, a server cluster in which multiple servers cooperate to operate, and the like.

[0041] The image popularity prediction system 1 includes: an image receiving device 11 , a convol...

Embodiment 2

[0091] Based on the optional solutions in the first embodiment above, this embodiment provides a convolutional neural network-based lung tumor recognition system. Wherein, the lung tumor identification system based on the convolutional neural network is applied in a CT image scanning instrument of a hospital, or an image processing device (such as a server) connected with the CT image scanning instrument. For a CT image scanning instrument, at least a CT scanning device is included. The CT scanning device scans the parts to be examined (such as the lungs, the brain, or the whole body) of the living body in a cross-sectional manner along the spatial axis, and obtains corresponding multiple CT stacked images. Correspondingly, the convolutional neural network-based lung tumor identification system 3 includes: an image library 31 , an image heat prediction system 32 combined with any optional solution in the first embodiment above, and a control device 33 . Such as Figure 4 sho...

Embodiment 3

[0106] Such as Figure 5 As shown, the present invention provides a flow chart of an image heat prediction method. The image heat prediction method is mainly performed by a prediction system. Wherein the prediction system includes software and hardware installed in computer equipment. Wherein, the hardware in the computer device includes: an input unit, a processing unit, a storage unit, a cache, and a display unit, etc., wherein the processing unit may include a chip or an integrated circuit dedicated to a convolutional neural network and include A computer program for the convolutional neural network algorithm. The processing unit allocates the operation of each hardware through the time sequence set by the program, so as to execute the functions of the following devices. Wherein, the computer equipment includes, but is not limited to: a single server, a server cluster in which multiple servers cooperate to operate, and the like.

[0107] The image heat prediction method...

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Abstract

The invention provides a lung tumor recognition system and method based on a convolutional neural network. The lung tumor recognition method based on the convolutional neural network includes the steps that multiple cascading images collected along a preset dimensionality are received; the cascading images are blocked according to the rule that moving is the same in the same window, the image blocks in the cascading images corresponding to the same window position are combined and convolved to obtain feature image blocks, and down-sampling is conducted on each feature image block; the feature image blocks subjected to down-sampling are subjected to up-sampling; a heat degree prediction map is drawn on the basis of probability that each pixel in the heat degree prediction map corresponds to at least one feature image block subjected to up-sampling may be true or false. The system and method effectively increase the recognition rate of local areas of the cascading images with spatial association.

Description

technical field [0001] The present invention relates to an image processing technology, in particular to a lung tumor recognition system and method based on a convolutional neural network. Background technique [0002] In recent years, convolutional neural networks have demonstrated their advantages in image feature recognition tasks. For example, during the construction of a 2D image to a 3D image, a convolutional neural network is used to perform bad block screening on 2D image blocks. Most of the current convolutional neural networks use deep learning methods to classify images as true or false. As a model with a slightly special structure, the fully convolutional neural network has made great progress in local recognition tasks, including object bounding box detection, prediction of key parts and key points of objects, etc. These prediction methods still use the method of learning the classifier to improve the recognition and classification of image blocks. The disadv...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06T7/00
CPCG06T7/0012G06T2207/30064G06T2207/30061G06T2207/10081G06V40/10G06V10/44G06V10/56G06F18/24
Inventor 徐葳冯迭乔
Owner TSINGHUA UNIV
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