DocumentCode :
548707
Title :
An experimental framework for learning the medical image diagnosis
Author :
Ion, Anca Loredana ; Udristoiu, Stefan
Author_Institution :
Fac. of Autom., Comput. & Electron., Univ. of Craiova, Dolj, Romania
fYear :
2011
fDate :
27-30 June 2011
Firstpage :
465
Lastpage :
470
Abstract :
In this paper we propose a framework in which some methods are compared to achieve the medical diagnosis based on image analysis. The proposed framework includes besides components for the extraction of low-level features, methods for the integration of semantic knowledge about the medical diagnosis into the retrieval process. So, the paper approaches modalities for learning the medical diagnosis using low-level characteristics automatically extracted from the visual content to generate high-level concepts by means of semantic association rules. The experiments through this experimental framework were realized on medical collections of images.
Keywords :
data mining; feature extraction; image retrieval; learning (artificial intelligence); medical image processing; patient diagnosis; learning framework; low level feature extraction; medical image diagnosis; retrieval process; semantic association rules; semantic knowledge; Cancer; Feature extraction; Image color analysis; Medical diagnostic imaging; Semantics; Visualization; Medical image diagnosis; association rules; content-based visual retrieval; medical image mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology Interfaces (ITI), Proceedings of the ITI 2011 33rd International Conference on
Conference_Location :
Dubrovnik
ISSN :
1330-1012
Print_ISBN :
978-1-61284-897-6
Electronic_ISBN :
1330-1012
Type :
conf
Filename :
5974067
Link To Document :
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