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Application of Artificial Immune System Approach in MRI Classification

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Abstract

Numerous scholars have submitted the theory and research of artificial immune systems (AISs) in recent years. Although AIS has been used in various fields, applying the AIS to medical images is very rare. The purpose of this study is using the clonal selection algorithm (CSA) of artificial immune systems for classifying the brain MRI, and displaying a single organism image which can finally offer faster organism reference information to a doctor; hence reducing the time to ascertain large number of images, so that the doctor can diagnose the nidus more efficiently and accurately. In order to verify the feasibility and efficiency of this method, we adopt statistical theory for manifold assessment and compare with the perceptron network of double layers, FCM method. The result proves that the method of this study is both feasible and useful.

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Correspondence to Chuin-Mu Wang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Wang, C., Kuo, C., Lin, C. et al. Application of Artificial Immune System Approach in MRI Classification. EURASIP J. Adv. Signal Process. 2008, 547684 (2008) doi:10.1155/2008/547684

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Keywords

  • Manifold
  • Double Layer
  • Medical Image
  • System Approach
  • Statistical Theory