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

• Autorzy: Naoui Mohammed Anouar , Lejdel Brahim , Ayad Mouloud , Belkeiri Riad , Khaouazm Abd Sattar

Identyfikatory

DOI

10.1515/bams-2019-0043

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

big data; deep learning; healthcare system; social network; X-ray image

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2020
• Tom: Vol. 16, no. 1
• Strony: art. no. 20190043
• Opis fizyczny: Bibliogr. 48 poz., rys., tab.

Twórcy

autor

Naoui Mohammed Anouar

• LIMPAF Laboratory, Computer Science Department, Faculty of Sciences and Applied Sciences, University of Bouira, Bouira, Algeria; El-Oued University, El-Oued, Algeria

autor

Lejdel Brahim

• Computer Science Department, El-Oued University, El-Oued, Algeria

autor

Ayad Mouloud

• University of Bouira, Bouira, Algeria

autor

Belkeiri Riad

• Computer Science Department, El-Oued University, El-Oued, Algeria

autor

Khaouazm Abd Sattar

• Computer Science Department, El-Oued University, El-Oued, Algeria

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).