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Pulmonary nodule detection method based on convolutional neural network

A technology of convolutional neural network and detection method, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve the problems of cumbersome steps, heavy workload, slow processing speed, etc., to reduce the number of parameters and accurately classify effects, performance-enhancing effects

Pending Publication Date: 2021-01-22
JIANGNAN UNIV
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Problems solved by technology

However, due to the small size of pulmonary nodules, the traditional C-image pulmonary nodule detection method is not only cumbersome and slow in processing; clinically, the pulmonary nodule detection method is for doctors to identify whether a patient has pulmonary nodules by observing the CT images of the lungs , and usually a patient has a large number of complete CT sequences, the workload is heavy, and it is easy to be missed

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  • Pulmonary nodule detection method based on convolutional neural network
  • Pulmonary nodule detection method based on convolutional neural network
  • Pulmonary nodule detection method based on convolutional neural network

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Embodiment Construction

[0027] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0028] The present invention has constructed a kind of pulmonary nodule detection method based on convolutional neural network, and concrete steps are as follows:

[0029] Step 1: Segment the lung parenchyma

[0030] The 3D U-Net network was used to segment the lungs to extract the lung parenchyma, and the obtained 3D image of the lung parenchyma was divided into two-dimensional slice images according to the Z axis. The encoding module of the 3D U-Net network includes 5 sets of convolutions and 4 downsampling operations. The corresponding decoding part also includes 4 sets of convolutions and 4 upsampling operations.

[0031] The decoding part adopts the method of skip connection to splicing the encoded high-level semantic information and the decoded low-level semantic information to ensure that the final fused feature map incorpora...

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Abstract

The invention discloses a pulmonary nodule detection method based on a convolutional neural network, and belongs to the field of deep learning image detection. After the pulmonary parenchyma is extracted, the attention of the neural network to the target area can be effectively improved, and the number of parameters can be effectively reduced. After the step of preliminary candidate nodule detection is completed, the target area close to the center of the pulmonary nodule is selected for nodule detection, the overall detection range is narrowed, a relatively accurate classification effect is obtained, and the classification performance is improved.

Description

technical field [0001] The invention belongs to the field of deep learning image detection, and relates to a method for detecting pulmonary nodules based on a convolutional neural network. Background technique [0002] Early pulmonary nodules are usually small and have no fixed shape, so they are difficult to distinguish with the naked eye. When benign pulmonary nodules continue to develop into malignant pulmonary nodules, lung cancer develops. In reality, lung nodules are usually detected from lung CT images. Pulmonary nodules are an early manifestation of lung cancer, and the detection of pulmonary nodules is of great significance for predicting lung cancer. However, due to the small size of pulmonary nodules, the traditional C-image pulmonary nodule detection method is not only cumbersome and slow in processing; clinically, the pulmonary nodule detection method is for doctors to identify whether a patient has pulmonary nodules by observing the CT images of the lungs , a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/194G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06T7/194G06N3/08G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30064G06N3/047G06N3/045G06F18/241
Inventor 肖志勇钱宝鑫
Owner JIANGNAN UNIV
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