A chromatic image understanding system for lung cancer cell identification based on fuzzy knowledge

Authors:
Yubin Yang;Shifu Chen;Hui Lin;Yukun Ye
Affiliations:
Joint Laboratory for Geoinformation Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, P. R., China;Joint Laboratory for Geoinformation Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong;Nanjing Bayi Hospital, Nanjing, P. R., China
Venue:
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Year:
2004

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An intelligent medical image understanding method using two-tier neural network ensembles

IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Transductive cost-sensitive lung cancer image classification

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Abstract

This paper presents an intelligent medical chromatic image understanding system for lung cancer cell identification based on fuzzy knowledge representation and reasoning. Following image analysis and a low-level feature extraction process, a two-layer rule-based fuzzy knowledge model is proposed to represent the domain knowledge needed for image understanding task. Experimental results show that the system achieves not only a high rate of overall correct identification, but also a low rate of false negative identification, that is, a low rate of identifying cancer cases to be normal ones, which is important in reducing false diagnosis cases.