A method for identifying pulmonary nodules by using a convolution neural network based on particle swarm algorithm optimization

Abstract

The invention belongs to the field of combining artificial intelligence with medical image analysis, and relates to a method for identifying pulmonary nodules by using a convolution neural network based on particle swarm algorithm optimization. The method comprises the following steps: obtaining CT slice images of patients' lungs; preprocessing CT slice images, and labeling the obtained CT slice images to form a data set; constructing convolution neural network; optimizing super parameters of convolution neural networks using particle swarm optimization; The optimized convolution neural network is trained by the labeled dataset. The features of pulmonary nodules were extracted by using the trained convolution neural network. The invention optimizes the convolution neural network through the particle swarm algorithm, solves the problem that the optimal super parameter of the convolution neural network is difficult to be selected manually, and has very good identification effect for lungnodules.

Description

technical field [0001] The invention belongs to the field of combining artificial intelligence and medical image analysis, and relates to a method for identifying pulmonary nodules based on a convolutional neural network optimized by a particle swarm algorithm. Background technique [0002] At present, computed tomography (CT) is a commonly used technology for effectively screening early lung cancer. The development of CT technology has significantly improved the detection rate of early lung cancer. Compared with conventional X-ray photography, we can see higher-resolution images of lung anatomy under CT; but for radiologists, observing and interpreting these images is a complex and tedious task. This is because in complex CT images, pulmonary nodules appear similar to some lung structures, are low-density and small in size, and may be located very close to or closely related to blood vessels or lung boundaries. The borders are connected. Second, another factor is the larg...

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Problems solved by technology

[0003] Aiming at the deficiencies of the prior art, the present invention proposes a method for identifying pulmonary nodules based on the convolutional neural network optimized by the particle swarm optimization algorithm. It is difficult to manually select the problem, and it has a good recognition effect on pulmonary nodules.

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