Frontiers in Medical Science Research, 2022, 4(10); doi: 10.25236/FMSR.2022.041009.
Xuan Ren, Menghua Chen, Ruijie Du, Weijie Xing
School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu, 200093, Shanghai, China
Breast cancer is a malignant tumor mostly found in the female population, and it is important to improve the survival rate of patients by determining its benignity and malignancy at the early stage of the disease through non-invasive imaging, so that appropriate treatment can be adopted. In this paper, we tested the application of the deep learning model of residual network (Resnet) in the diagnosis of breast tumors by Dynamic Contrast-Enhanced MRI (DCE-MRI), and compared three network models of different depths, Resnet50, Resnet34, and Resnet18, to find the best way to achieve the diagnosis of breast tumors. With the number of network layers increasing the depth of the network increases, and the phenomenon of overfitting occurs. The results showed that Resnet18 was the most accurate method to classify benign and malignant tumor images. The experimental results showed that the accuracy of Resnet18 network was higher, reaching 96.4%.
tumor of breast, Benign and malignant classification, Resnet
Xuan Ren, Menghua Chen, Ruijie Du, Weijie Xing. Differential Diagnosis of Benign and Malignant Breast Tumors Based on DCE-MRI. Frontiers in Medical Science Research (2022) Vol. 4, Issue 10: 49-55. https://doi.org/10.25236/FMSR.2022.041009.
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