Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7693
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dc.contributor.authorChan, Samuel W. K.en_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorWong, W. S. Felixen_US
dc.date.accessioned2023-03-30T05:19:53Z-
dc.date.available2023-03-30T05:19:53Z-
dc.date.issued1996-
dc.identifier.citationApplied Artificial Intelligence, 1996, vol.10 (5), pp. 407 - 438en_US
dc.identifier.issn08839514-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7693-
dc.description.abstractIn this article the design and implementation of a high-level image diagnosis system are described. A knowledge-based system, OOI, has been developed to incorporate the image processing abilities. It employs a robust control strategy in the object-oriented approach that minimizes the amount of domain-specific control knowledge. Knowledge objects are constructed that embed specialized methods or metaknowledge in image processing. They work independently of each other. The highly modular architecture allows the knowledge engineers to modify the knowledge without worrying about any unexpected side effects. Reasoning in OOI proceeds in both top-down and in bottom-up schemes. The system has been tested in medical image analysis. Cancer cell lesion classification is used as an application domain to illustrate how image diagnosis can be implemented in our system. Results attest to the efficacy of this framework in both image recognition and diagnosis. © 1996 Taylor & Francis Group, LLC.en_US
dc.language.isoenen_US
dc.relation.ispartofApplied Artificial Intelligenceen_US
dc.titleObject-oriented knowledge-based system for image diagnosisen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1080/088395196118489-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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