Medical image classfication using an efficient data mining technique
作者： S.M. Khan , R. Islam , M.U. Chowdhury
DOI: 10.1109/ICMLA.2004.1383541
关键词: Computer science 、 Feature selection 、 Data manipulation language 、 Knowledge extraction 、 Decision tree 、 Data mining 、 Decision tree learning 、 Visualization 、 Data visualization 、 Health care 、 Field (computer science)
摘要: Data 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 extraction techniques aimed at gaining insight into the relationships patterns hidden in Availability digital within picture archiving communication systems raises a possibility health care research enhancement associated with manipulation, processing handling by computers.That basis for computer-assisted radiology development. Further development use new intelligent capabilities such as multimedia support order discover relevant diagnosis. very useful if results can be communicated humans understandable way. In this paper, we present our work on medical image systems. We investigate efficient technique, decision tree, learn analysis. apply method classification x-ray images lung cancer The proposed technique based inductive tree learning algorithm has low complexity high transparency accuracy. show robust, accurate, fast, it produces comprehensible structure, summarizing induces.
sci-hub.se 参考文章(11)
Usama M. Fayyad, Mining Databases: Towards Algorithms for Knowledge Discovery. IEEE Data(base) Engineering Bulletin. ,vol. 21, pp. 39- 48 ,(1998)
K Adelhard, S Nissen-Meyer, C Pistitsch, U Fink, M Reiser, None, Functional Requirements for a HIS-RIS-PACS-Interface Design, Including Integration of “Old” Modalities Methods of Information in Medicine. ,vol. 38, pp. 1- 8 ,(1999) , 10.1055/S-0038-1634149
Huan Liu, Rudy Setiono, Incremental Feature Selection Applied Intelligence. ,vol. 9, pp. 217- 230 ,(1998) , 10.1023/A:1008363719778
Mark Andrew Hall, Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning international conference on machine learning. pp. 359- 366 ,(2000)
John H. Boose, David B. Shema, Jeffrey M. Bradshaw, Recent progress in AQUINAS: A knowledge acquisition workbench Knowledge Acquisition. ,vol. 1, pp. 185- 214 ,(1989) , 10.1016/S1042-8143(89)80019-6
Huan Liu, H. Motoda, Feature Transformation And Subset Selection IEEE Intelligent Systems and their Applications. ,vol. 13, pp. 26- 28 ,(1998) , 10.1109/MIS.1998.671088
R. Setiono, Huan Liu, A connectionist approach to generating oblique decision trees systems man and cybernetics. ,vol. 29, pp. 440- 444 ,(1999) , 10.1109/3477.764880
Huan Liu, Rudy Setiono, Some issues on scalable feature selection1This is an extended version of the paper presented at the Fourth World Congress of Expert Systems: Application of Advanced Information Technologies held in Mexico City in March 1998.1 Expert Systems with Applications. ,vol. 15, pp. 333- 339 ,(1998) , 10.1016/S0957-4174(98)90049-5
Traeden J, Standardize and upgrade a mixed computing environment. Health management technology. ,vol. 20, pp. 24- ,(1999)
Chaney Dm, Coordinate your plans for EMR and PACS investments. Health management technology. ,vol. 20, ,(1999)