ML Classification Methods Comparison for Breast Cancer Diagnosis in Clinical Application Field
-
Published:2023-03-30
Issue:
Volume:41
Page:87-92
-
ISSN:2791-0210
-
Container-title:Highlights in Science, Engineering and Technology
-
language:
-
Short-container-title:HSET
Abstract
With the development of computing resources, deep learning method has made great progress in the field of image recognition. And as a result, deep learning-based image detection methods have been able to make great breakthroughs in medical fields such as disease recognition, microbiological testing, bacterial quantification, and tumour detection. With the advancement of in the ML field, the computer-aid diagnosis system has been improving. This paper reviews basic CAD ML system, and the recently improved DL system. In general, both methods offer the ability to assist the medical expert in giving them opinions. Deep learning, on the other hand, shows promising improvement in breast cancer diagnosis, but still with some limitations, such as requirement of high computational power and cost, as well as data scarcity. The accuracy rate of DL can reach up to 97%, which is the best in the field. Thus, both effectively reduce false positive rate of detecting breast cancer, improving the performance for detection and diagnosis.
Publisher
Darcy & Roy Press Co. Ltd.
Reference15 articles.
1. Mettlin, C. J., Menck, H. R., Winchester, D. P., & Murphy, G. P. (1997). A comparison of breast, colorectal, lung, and prostate cancers reported to the National Cancer Data Base and the Surveillance, Epidemiology, and End Results Program. Cancer: Interdisciplinary International Journal of the American Cancer Society, 79(10), 2052-2061. 2. Ayer, T., Chen, Q., & Burnside, E. S. (2013). Artificial neural networks in mammography interpretation and diagnostic decision making. Computational and mathematical methods in medicine, 2013. 3. Biltawi, M., Al-Najdawi, N. I. J. A. D., & Tedmori, S. A. R. A. (2012, December). Mammogram enhancement and segmentation methods: classification, analysis, and evaluation. In The 13th international Arab conference on information technology. 4. Ponraj, D. N., Jenifer, M. E., Poongodi, P., & Manoharan, J. S. (2011). A survey on the preprocessing techniques of mammogram for the detection of breast cancer. Journal of Emerging Trends in Computing and Information Sciences, 2(12), 656-664. 5. Ferguson, C., Araújo, D., Faulk, L., Gou, Y., Hamelers, A., Huang, Z., ... & McEntyre, J. (2021). Europe PMC in 2020. Nucleic acids research, 49(D1), D1507-D1514.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|