Integrating deep learning, social networks, and big data for healthcare system

被引:0
|
作者
Naoui, Mohammed Anouar [1 ,2 ]
Lejdel, Brahim [3 ]
Ayad, Mouloud [4 ]
Belkeiri, Riad [3 ]
Khaouazm, Abd Sattar [3 ]
机构
[1] Univ Bouira, Fac Sci & Appl Sci, Comp Sci Dept, LIMPAF Lab, Bouira, Algeria
[2] El Oued Univ, El Oued, Algeria
[3] El Oued Univ, Comp Sci Dept, El Oued, Algeria
[4] Univ Bouira, Bouira, Algeria
关键词
big data; deep learning; healthcare system; social network; X-ray image; NEURAL-NETWORK; HEART-DISEASE; DIAGNOSIS;
D O I
10.1515/bams-2019-0043
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper aims to propose a deep learning model based on big data for the healthcare system to predict social network data. Social network users post large amounts of healthcare information on a daily basis and at the same time hospitals and medical laboratories store very large amounts of healthcare data, such as X-rays. The authors provide an architecture that can integrate deep learning, social networks, and big data. Deep learning is one of the most challenging areas of research and is becoming increasingly popular in the health sector. It uses deep analysis to extract knowledge with optimum precision. The proposed architecture consists of three layers: the deep learning layer, the big data layer, and the social networks layer. The big data layer includes data for health care, such as X-ray images. For the deep learning layer, three Convolution Neuronal Network models are proposed for X-ray image classification. As a result, social network layer users can access the proposed system to predict their X-ray image posts.
引用
收藏
页数:13
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