Deep Learning Model Aids Breast Cancer Detection
DOI:
https://doi.org/10.54097/fcis.v6i1.18Keywords:
Breast Cancer Detection, Medical Image Analyze, Vision Model, Deep LearningAbstract
Breast cancer, a lumpy nodule or granular calcified tissue caused by cancerous changes in chest tissue, has become one of the most prevalent cancers. Due to the location and structure of the tumor, it can be detected directly by ultrasound or X-ray and is less likely to spread to other parts of the body than tumors in other parts of the body. Considering the huge number of sick people, the resources required for a full census would be enormous, but thanks to the rapid development of medical image processing technology in recent years, assisted diagnosis through deep learning models has gradually become more widely accepted. For detection models, higher accuracy means lower misdiagnosis rates and timely treatment for patients. Therefore, in this paper, we first specify the diagnose as a binary classification problem and then introduce a new pooling scheme and training method to achieve better results compared to the traditional network backbone in the past.
Downloads
References
Fear, Elise C., Paul M. Meaney, and Maria A. Stuchly. "Microwaves for breast cancer detection?." IEEE potentials 22.1 (2003): 12-18.
Moore, S. K. (2001). Better breast cancer detection. Ieee Spectrum, 38(5), 50-54.
Sutanto, D. H., & Ghani, M. K. A. (2015). A benchmark of classification framework for non-communicable disease prediction: a review. ARPN J Eng Appl Sci, 10, 9941-9955.
Wang Y, Yang K, Wan W, Mei H. Adaptive energy saving algorithms for Internet of Things devices integrating end and edge strategies. Trans Emerg Telecommun Technol. 2020..
Singh, B. K., Verma, K., & Thoke, A. S. (2016). Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images. Expert Systems with Applications, 66, 114-123.
Chen, H., Gao, M., Zhang, Y., Liang, W., & Zou, X. (2019). Attention-based multi-NMF deep neural network with multimodality data for breast cancer prognosis model. BioMed research international, 2019.
Y. Wang, K. Yang, W. Wan, Y. Zhang and Q. Liu, "Energy-Efficient Data and Energy Integrated Management Strategy for IoT Devices Based on RF Energy Harvesting," in IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13640-13651, 1 Sept.1, 2021, doi: 10.1109/JIOT.2021.3068040..
Hao Hu, Shulin Li, Jiaxin Huang, Bo Liu, and Change Che. Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10):42–46, 2023.
Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278–280.
Hu H, Li S, Huang J, et al. Casting Product Image Data for Quality Inspection with Xception and Data Augmentation[J]. Journal of Theory and Practice of Engineering Science, 2023, 3(10): 42-46.
Che C, Liu B, Li S, et al. Deep Learning for Precise Robot Position Prediction in Logistics[J]. Journal of Theory and Practice of Engineering Science, 2023, 3(10): 36-41.
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).
Tianbo, Song, Hu Weijun, Cai Jiangfeng, Liu Weijia, Yuan Quan, and He Kun. "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition." In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 834-837. IEEE, 2023.
Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu. Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10):36–41, 2023.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281–285.
S. Tianbo, H. Weijun, C. Jiangfeng, L. Weijia, Y. Quan and H. Kun, "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition," 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China, 2023, pp. 834-837, doi: 10.1109/ICCECE58074.2023.10135464.
