Cloud Colonography: Distributed Medical Testbed over Cloud

Cloud Colonography is proposed in this paper, using different types of cloud computing environments. Databases from the Computed Tomographic Colonography (CTC) screening tests among several hospitals are explored. These networked databases are going to be available in the near future via cloud compu...

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Bibliographic Details
Published in:IEEE transactions on cloud computing Vol. 8; no. 2; pp. 495 - 507
Main Authors: Motai, Yuichi, Henderson, Eric, Siddique, Nahian Alam, Yoshida, Hiroyuki
Format: Journal Article
Language:English
Published: Piscataway IEEE Computer Society 01.04.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cloud computing
Cloud service
Colonography
computed tomographic colonography
Computing time
Distributed databases
Eigenvalues and eigenfunctions
group learning
Handles
health care
Kernel
kernel feature analysis
Parallel processing
Principal components analysis
Servers
ISSN:2168-7161, 2372-0018
Online Access:Get full text
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Summary:Cloud Colonography is proposed in this paper, using different types of cloud computing environments. Databases from the Computed Tomographic Colonography (CTC) screening tests among several hospitals are explored. These networked databases are going to be available in the near future via cloud computing technologies. Associated Multiple Databases (AMD) was developed in this study to handle multiple CTC databases. When AMD is used for assembling databases, it can achieve very high classification accuracy. The proposed AMD has the potential to play a role of the core classifier in the cloud computing framework. AMD for multiple institutions databases yields high detection performance of polyps using Kernel principal component analysis (KPCA). Two cases in the proposed cloud platform are private and public. We adapted a university cluster as a private platform, and Amazon Elastic Compute Cloud (EC2) as a public. The computation time, memory usage, and running costs were compared using three representative databases between private and public cloud environments. The proposed parallel processing modules improved the computation time, especially in the public cloud environments. The successful development of a cloud computing environment that handles large amounts of data will make Cloud Colonography feasible for a new health care service.
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ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2015.2481414