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Lung nodule detection method based on 2D convolutional neural network

A convolutional neural network and detection method technology, which is applied in biological neural network models, neural architectures, instruments, etc., can solve the problems of complex processing process and occupy a lot of resources, achieve simple calculation process, improve detection accuracy, and speed up detection efficiency. Effect

Active Publication Date: 2018-10-16
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

[0003] In order to combine the three-dimensional properties of CT images, the existing pulmonary nodule detection methods are usually based on 3D convolutional neural network for pulmonary nodule detection, the processing process is more complicated, and it takes up more resources. Not necessarily the same, the unified 3D convolution processing method is not applicable

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  • Lung nodule detection method based on 2D convolutional neural network
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[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. 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.

[0017] Embodiments of the present invention provide a method for detecting pulmonary nodules based on a 2D convolutional neural network, such as figure 1 As shown, it mainly includes:

[0018] 1. Train the suspected pulmonary nodule detection model.

[0019] For each CT image in the training set, according to the marked nodule position, extract the slice image of the nodule center and its adjacent two slice images; for each slice image, train a su...

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Abstract

The invention discloses a lung nodule detection method based on a 2D convolutional neural network. The method improves the detection accuracy by detecting suspected pulmonary nodules and reducing false positives. Meanwhile, the whole detection process can be automatically completed, and the detection efficiency of the lung nodules is accelerated.

Description

technical field [0001] The invention relates to the technical field of intelligent medical image analysis, in particular to a method for detecting pulmonary nodules based on a 2D convolutional neural network. Background technique [0002] Lung CT images are three-dimensional images, and each image contains a series of multiple axial slices of the chest cavity. Each 3D image is composed of a different number of 2D images. The number of its 2D images can vary based on different factors, such as the scanning machine, the scanned user. [0003] In order to combine the three-dimensional properties of CT images, the existing pulmonary nodule detection methods are usually based on 3D convolutional neural network for pulmonary nodule detection, the processing process is more complicated, and it takes up more resources. Not necessarily the same, a unified 3D convolution processing method is not applicable. Contents of the invention [0004] The purpose of the present invention i...

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Application Information

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IPC IPC(8): G06K9/32G06K9/62G06N3/04
CPCG06V10/25G06V2201/03G06N3/045G06F18/214
Inventor 谢洪涛张勇东
Owner UNIV OF SCI & TECH OF CHINA
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