A method for establishing a pulmonary nodule detection device based on 3D convolutional neural network

Abstract

The invention discloses a method for establishing a pulmonary nodule detection device on the basis of a 3D convolutional neural network. The method comprises the steps of establishing a training sample; establishing a pulmonary nodule detection network, wherein a pulmonary nodule segmentation network comprises a convolution unit, a 64*64*64(32) residual convolution unit A, a 32*32*32 (64) residualconvolution unit B, a 16*16*16 (64) residual convolution unit C, an 8*8*8 (64) residual convolution unit D and a 16*16*16 (64) residual convolution unit E which are connected with one another in sequence, output feature graphs of the residual convolution unit E and output feature graphs of the residual convolution unit C are spliced according to channels and then input to the residual convolutionunit F, and output feature graphs of the residual convolution unit F and output feature graphs of the residual convolution unit B are spliced according to channels and then input to an RPN network toachieve pulmonary nodule detection of the input graphs; training the pulmonary nodule detection network and obtaining the pulmonary nodule detection device.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a method for establishing a pulmonary nodule detection device based on a 3D convolutional neural network. Background technique [0002] There are many existing methods for detecting pulmonary nodules in lung CT images using deep learning algorithms, but the detection accuracy is not high. The main reasons for the low accuracy are: [0003] (1) The recall rate in the detection stage is lower than that of some special types of pulmonary nodules, resulting in missed detection and low detection accuracy. [0004] (2) The size of pulmonary nodules is uneven, and smaller pulmonary nodules are easily overlooked. [0005] Based on the above two reasons, the typicality and representativeness of pulmonary nodules detected and segmented by deep learning algorithms are insufficient. [0006] Therefore, improving the accuracy of pulmonary nodule detection and training the networ...

AI Technical Summary

Problems solved by technology

[0003] (1) The recall rate in the detection stage is lower than that of some special types of pulmonary nodules, resulting in missed detection and low detection accuracy

[0004] (2) The size of pulmonary nodules is uneven, and smaller pulmonary nodules are easily overlooked

[0005] Based on the above two reasons, the typicality and representativeness of pulmonary nodules detected and segmented by deep learning algorithms are insufficient.

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