International Journal of Electronic Engineering and Computer Science
Articles Information
International Journal of Electronic Engineering and Computer Science, Vol.5, No.2, Jun. 2020, Pub. Date: Nov. 6, 2020
Cancer Recognition Based on Deep Residual Network
Pages: 15-21 Views: 72 Downloads: 15
[01] Xinlin Yang, School of Information Engineering, China University of Geosciences, Beijing, China.
[02] Guanyi Li, School of Information Engineering, China University of Geosciences, Beijing, China.
[03] Xiaotong Li, School of Information Engineering, China University of Geosciences, Beijing, China.
[04] Mei Li, School of Information Engineering, China University of Geosciences, Beijing, China.
Cancer has become a great threat to people's health due to its high mortality rate. According to the global statistics in 2018, the incidence and mortality of male lung cancer account for the first place in malignant tumors. The recognition of early cancer by medical imaging technology can reduce the mortality of cancer. In image recognition, many image features that are difficult to be detected by human eyes can be found with the help of deep neural network. The application of deep residual network structure to medical image recognition can improve the accuracy of recognition and alleviate the problem of gradient disappearance caused by increasing depth in deep neural network. This paper USES RESnet-50 in the deep residual network to identify the categories of pulmonary nodules. According to the label information on the picture, label the picture of pulmonary nodules to be identified by type. The model was trained through the LIDC-IDRI data set, and the samples on the data set were tested using the trained RESnet-50 network architecture. The final identification accuracy was stable at 0.93. By comparing the recognition accuracy of other networks, the architecture can improve the recognition accuracy. The technology will reduce the work intensity of doctors to reduce the rate of misdiagnosis, and realize the early detection and treatment of cancer.
Deep Learning, Convolutional Neural Networks, ResNet-50, Residual Network, Lung Nodules, Cancer Recognition
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