| Autor: |
Michael J. Cree, Yifan Chen, Yue Sun, Yue Xiao, Zheng Gong |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 IEEE 21st International Conference on Nanotechnology (NANO). |
| DOI: |
10.1109/nano51122.2021.9514336 |
| Popis: |
This paper proposes a new fuzzy-logic-inspired multi-contrast-agent strategy (MCAS) for optimal tumor classification. The proposed strategy accounts for the competitive and symbiotic relationships among multiple contrast agents through a sequential logic circuit analysis. Furthermore, the strategy enables an intuitive yet systematic way to analyze the tumor classification vagueness and ambiguous uncertainties and optimize the utilization of multiple agents through a fuzzy comprehensive evaluation. A numerical example is used to demonstrate how the classification performance in terms of decision-making fuzziness is significantly improved with an optimal “cocktail recipe” methodology using the proposed MCAS. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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