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Multi-classifier detection of lung nodules based on convolutional neural network
Author(s): Yang Yang; Yang Xu; WenFeng Shen; Feng Qiu
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Paper Abstract

With the application of deep learning in the detection of medical images, computer aided systems have become less and less effective in detecting auxiliary nodules. The feature extraction of nodule detection is crucial to the judgment of the final result, which is also true in the automatic classifier. The pre-trained convolutional neural network has a certain degree of success in depth feature extraction. In this paper, we used Unet, a full-volume machine network instead of the traditional convolutional neural network to extract features. We used Resnet to construct a classifier for the extracted features and use Adaboost for integrated learning, which ultimately achieved the best accuracy,up to 72.6%. Compared with the use of traditional classifiers such as SVM, BPNN and other methods, the proposed method has better advantages in feature extraction and detection speed.

Paper Details

Date Published: 29 October 2018
PDF: 6 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361F (29 October 2018); doi: 10.1117/12.2326991
Show Author Affiliations
Yang Yang, Shanghai Univ. (China)
Yang Xu, Shanghai Univ. (China)
WenFeng Shen, Shanghai Univ. (China)
Feng Qiu, Shanghai Univ. (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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