Computational Intelligence for Medical Imaging Simulations.
This paper describes how to simulate medical imaging by computational intelligence to explore areas that cannot be easily achieved by traditional ways, including genes and proteins simulations related to cancer development and immunity. This paper has presented simulations and virtual inspections of...
| Published in: | Journal of Medical Systems Vol. 42; no. 1; pp. 1 - 13 |
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| Format: | algorithm research tables/charts Journal Article |
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Springer Nature
Jan2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127145175&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127145175 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2018 vid: 42 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127145175 127145175 127145175 10.1007/s10916-017-0861-x 127145175 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computational Intelligence for Medical Imaging Simulations. aug: au: Chang, Victor affil: International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China sug: subj: Computer Simulation Diagnostic Imaging Artificial Intelligence Algorithms Neoplasms Familial and Genetic Brain Physiology ab: This paper describes how to simulate medical imaging by computational intelligence to explore areas that cannot be easily achieved by traditional ways, including genes and proteins simulations related to cancer development and immunity. This paper has presented simulations and virtual inspections of BIRC3, BIRC6, CCL4, KLKB1 and CYP2A6 with their outputs and explanations, as well as brain segment intensity due to dancing. Our proposed MapReduce framework with the fusion algorithm can simulate medical imaging. The concept is very similar to the digital surface theories to simulate how biological units can get together to form bigger units, until the formation of the entire unit of biological subject. The M-Fusion and M-Update function by the fusion algorithm can achieve a good performance evaluation which can process and visualize up to 40 GB of data within 600 s. We conclude that computational intelligence can provide effective and efficient healthcare research offered by simulations and visualization. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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