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.
 Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022 Jan;72(1):7-33. doi: 10.3322/caac.21708. Epub 2022 Jan 12. PMID: 35020204.
 Global Cancer Statistics Report 2020[J]. Chinese Journal of Preventive Medicine,2021,55(03):398-398.
 Liu Zongchao, LI Zhexuan, ZHANG Yang, ZHOU Tong, ZHANG Jingying, You Weicheng, Pan Kaifeng, LI Wenqing. Interpretation on the report of Global Cancer Statistics 2020[J]. Journal of Multidisciplinary Cancer Management (Electronic Version),2021,7(02):1-14.
 Zhang Yacong, LV Zhangyan, SONG Fangfang, Chen Kexin. Trends in incidence and mortality of breast cancer worldwide and in China[J]. Journal of Multidisciplinary Cancer Management (Electronic Version),2021,7(02):14-20.
 Yubei Huang, Zhongsheng Tong, Kexin Chen, Ying Wang, Peifang Liu, Lin Gu, Juntian Liu, Jinpu Yu, Fengju Song, Wenhua Zhao, Yehui Shi, Hui Li, Huaiyuan Xiao, Xishan Hao. Interpretation of guideline for breast cancer screening in Chinese women[J]. Chinese Journal of Clinical Oncology, 2019, 46(09):432-440.
 Yang Lei, Tang Can. CAD machine diagnosis system in ultrasonic diagnosis of breast cancer [J]. The Journal of Practical Medicine, 2022,38(01):106-110.
 Nie Shengdong, Wei Chuanling, Zhang Xiaobing. Review of breast cancer computer-aided detection methods based on magnetic resonance imaging [J/OL]. Journal of University of Shanghai for Science and Technology:1-11[2022-03-16].DOI:10.13255/j.cnki.jusst.20211225001.
 Liu Yan. RESEARCH ON KEY TECHNOLOGIES OF BENIGN AND MALIGNANT CLASSIFICATION BASED ON DUAL-MODE BREAST ULTRASOUND IMAGES [D]. Harbin Institute of Technology, 2013.
 George Y M, Zayed H H, Roushdy M I, et al. Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images [J]. IEEE Systems Journal, 2014, 8(3):949-964.
 Sun Mei, Yan Chuanbo, Zhang Yu, Bi Xuehua. Optimization and Classification of Ultrasound Image Features for Breast Tumors Based on Data Mining Algorithm [J]. Bulletin of Science and Technology, 2017, 33(10): 67-72. DOI: 10.13774/j.cnki.kjtb.2017.10.013.
 Qun Xia, Yangmei Cheng, Jinhua Hu, Juxia Huang, Yi Yu, Hongjuan Xie, Jun Wang.Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system [J].Mathematical Biosciences and Engineering, 2021,18(4):3680-3689.doi: 10.3934/mbe.2021184
 H. -C. Shin et al., "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1285-1298, May 2016, doi: 10.1109/TMI.2016.2528162.
 Nurwahidah Mamat, Wan Eny Zarina Wan Abdul Rahman, Shaharuddin Soh, Rozi Mahmud (2016). Evaluation of Performance for Different Filtering Methods in CT Brain Images. AIP Conference Proceedings. https://doi.org/10.1063/1.5055479
 Jose Bernal, Kaisar Kushibar, Daniel S. Asfaw, Sergi Valverde, Arnau Oliver, Robert Martí, Xavier Lladó, Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review, Artificial Intelligence in Medicine, Volume 95,2019, Pages 64-81,ISSN 0933-3657.
 Yu Dong. Benign and Malignant Classification of Nodules in Mammography Images [D]. Huazhong University of Science and Technology. 2020.