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.
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Yang, Y., Chen, S., Lin, H., Ye, Y. (2004). A Chromatic Image Understanding System for Lung Cancer Cell Identification Based on Fuzzy Knowledge. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_41
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DOI: https://doi.org/10.1007/978-3-540-24677-0_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
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