Multi-classifier detection of lung nodules based on convolutional neural network

Yang Yang; Yang Xu; WenFeng Shen; Feng Qiu

doi:10.1117/12.2326991

29 October 2018 Multi-classifier detection of lung nodules based on convolutional neural network

Yang Yang, Yang Xu, WenFeng Shen, Feng Qiu

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108361F (2018) https://doi.org/10.1117/12.2326991
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

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.

Citation Download Citation

Yang Yang, Yang Xu, WenFeng Shen, and Feng Qiu "Multi-classifier detection of lung nodules based on convolutional neural network", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361F (29 October 2018); https://doi.org/10.1117/12.2326991

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