基于Faster-RCNN的肺结节检测算法

doi:10.3969/j.issn.0258-8021.2020.02.01

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Abstract Aiming to overcome the problems of individual differences and the homograph in the detection of pulmonary nodules, this paper presented a method of automatic recognition of pulmonary nodules based on Faster-RCNN. By comparing the adaptability of the current in-depth learning model, a general strategy was proposed to continuously improve the detection rate of pulmonary nodules with the increase of the number of samples. First of all, the hardware and software environment for deep learning was built; next, the data interface was set to match with the network interface of Faster-RCNN. Secondly, the single-category classification network of Faster-RCNN was set up and the parameters were adjusted. Thirdly, the pulmonary nodules database containing 2 000 patients was utilized to train different feature extraction models (including ZF and VGG models), and the features of CT pictures in different networks were calculated. The test results, missed detection rate and detection accuracy were evaluated. Finally, the influence of different training numbers and data augmentation types on the final detection accuracy was analyzed. The accuracy rate of ZF model was 90.82%, the variance of accuracy rate was 13.30%; the detection accuracy of VGG model was 87.02%, the variance of accuracy rate was 37.10%. Taking into account the balance between the missed detection rate and detection accuracy rate, the ZF model showed small fluctuation variance, a slight low accuracy, and high detection precision. Therefore, the ZF model for pulmonary nodules was better than VGG model. Our proposed lung nodule detection technology has a good theoretical value and engineering application value.

Key words： faster-RCNN pulmonary nodule detection ZF model VGG model convolutional neural network

Received: 10 January 2019

PACS:

R318

Corresponding Authors: ^*E-mail: yyang@sdu.edu.cn

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	Song Shangling
	Yang Yang
	Li Xia
	Feng Hao

Cite this article:

Song Shangling,Yang Yang,Li Xia, et al. Pulmonary Nodule Detection Algorithm Based on Faster-RCNN[J]. Chinese Journal of Biomedical Engineering, 2020, 39(2): 129-136.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.02.01 OR http://cjbme.csbme.org/EN/Y2020/V39/I2/129

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