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 1,3; Tan, Liang 1,4; Lin, Long 5; Cheng, Xiaofan 5; Yi, Zhang 6; Sato, Takuro 2
刊名	IEEE WIRELESS COMMUNICATIONS
	2021-06-01
卷号	28 期号:3 页码:54-61
ISSN号	1536-1284
DOI	10.1109/MWC.001.2000374
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000675202200010
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/17482]
专题	中国科学院计算技术研究所
通讯作者	Tan, Liang
作者单位	1.Sichuan Normal Univ, Coll Comp Sci, Chengdu, Peoples R China 2.Waseda Univ, Waseda Res Inst Sci & Engn, Tokyo, Japan 3.Waseda Univ, Tokyo, Japan 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 5.Sichuan Normal Univ, Chengdu, Peoples R China 6.Sichuan Univ, Coll Comp Sci, Machine Intelligence Lab, Chengdu, Peoples R China
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.