DEEP-LEARNING-EMPOWERED BREAST CANCER AUXILIARY DIAGNOSIS FOR 5GB REMOTE E-HEALTH

doi:10.1109/MWC.001.2000374

	DEEP-LEARNING-EMPOWERED BREAST CANCER AUXILIARY DIAGNOSIS FOR 5GB REMOTE E-HEALTH
	Yu, Keping 1,2; Tan, Liang 1,3; Lin, Long 4; Cheng, Xiaofan 4; Yi, Zhang 5; Sato, Takuro 6
	2021-06-01
发表期刊	IEEE WIRELESS COMMUNICATIONS
ISSN	1536-1284
卷号	28 期号:3 页码:54-61
摘要	Breast cancer, the most common cancer in women, is receiving increasing attention. The lack of high-quality medical resources, especially highly skilled doctors, in remote areas makes the diagnosis of breast cancer inefficient and causes great harm to women. The emergence of remote e-health has improved the situation to a certain extent, but its capabilities are still hampered by technical limitations, which manifest in two main aspects. First, due to network bandwidth limitations, it is difficult to guarantee the real-time transmission of breast cancer pathology images between remote areas and cities. Second, the highly skilled breast cancer doctors at large city hospitals are not guaranteed to be available for online diagnosis at all times. To overcome these limitations, this article proposes a deep-learning-empowered breast cancer auxiliary diagnosis scheme for remote e-health supported by 5G technology and beyond (5GB remote e-health). In this scheme, breast pathology images are first received from major hospitals via 5G, and a deep learning model based on the Inception-v3 network is subjected to transfer learning to obtain a diagnostic model. This diagnostic model is then employed on edge servers for auxiliary diagnosis at remote area hospitals. A theoretical analysis and experimental results show that this solution not only overcomes the two problems mentioned above but also improves the diagnostic accuracy for breast cancer in remote areas to 98.19 percent.
DOI	10.1109/MWC.001.2000374
收录类别	SCI
语种	英语
资助项目	Japan Society for the Promotion of Science (JSPS)[JP18K18044] ; Japan Society for the Promotion of Science (JSPS)[JP21K17736] ; National Natural Science Foundation of China[61373162] ; Sichuan Science and Technology Department Project[2019YFG0183] ; Sichuan Provincial Key Laboratory Project[KJ201402]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000675202200010
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：58[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17483
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tan, Liang
作者单位	1.Sichuan Normal Univ, Coll Comp Sci, Chengdu, Peoples R China 2.Waseda Univ, Tokyo, Japan 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Sichuan Normal Univ, Chengdu, Peoples R China 5.Sichuan Univ, Coll Comp Sci, Machine Intelligence Lab, Chengdu, Peoples R China 6.Waseda Univ, Waseda Res Inst Sci & Engn, Tokyo, Japan
推荐引用方式 GB/T 7714	Yu, Keping,Tan, Liang,Lin, Long,et al. DEEP-LEARNING-EMPOWERED BREAST CANCER AUXILIARY DIAGNOSIS FOR 5GB REMOTE E-HEALTH[J]. IEEE WIRELESS COMMUNICATIONS,2021,28(3):54-61.
APA	Yu, Keping,Tan, Liang,Lin, Long,Cheng, Xiaofan,Yi, Zhang,&Sato, Takuro.(2021).DEEP-LEARNING-EMPOWERED BREAST CANCER AUXILIARY DIAGNOSIS FOR 5GB REMOTE E-HEALTH.IEEE WIRELESS COMMUNICATIONS,28(3),54-61.
MLA	Yu, Keping,et al."DEEP-LEARNING-EMPOWERED BREAST CANCER AUXILIARY DIAGNOSIS FOR 5GB REMOTE E-HEALTH".IEEE WIRELESS COMMUNICATIONS 28.3(2021):54-61.