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Medical image classification using an efficient data mining technique
conference contribution
posted on 2004-01-01, 00:00 authored by MD Rafiqul Islam, Morshed ChowdhuryMorshed Chowdhury, S KhanData mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.
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Title of proceedings
Complex 2004 : Proceedings of the 7th Asia-Pacific Complex Systems ConferenceEvent
Asia-Pacific Complex Systems Conference (7th : 2004 : Cairns, Qld.)Pagination
34 - 42Publisher
Central Queensland UniversityLocation
Cairns, Qld.Place of publication
Rockhampton, Qld.Start date
2004-12-06End date
2004-12-10ISBN-13
9781876674960ISBN-10
1876674962Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationEditor/Contributor(s)
R Stonier, Q Han, W LiUsage metrics
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