Improving the ranking quality of medical image retrieval using a genetic feature selection method

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Sergo Francisco da silva, et al.: Improving the ranking quality of medical image retrieval using a genetic feature selection method. Decision Support Systems, 51-4, 810 - 820, (2011)



In this paper, we take advantage of single-valued functions that evaluate rankings to develop a family of feature selection methods based on the genetic algorithm approach, tailored to improve the accuracy of content-based image retrieval systems. Experiments on three image datasets, comprising images of breast and lung nodules, showed that developing functions to evaluate the ranking quality allows improving retrieval performance. This approach produces significantly better results than those of other fitness function approaches, such as the traditional wrapper and than filter feature selection algorithms.

Extended Abstract


title = "Improving the ranking quality of medical image retrieval using a genetic feature selection method ",
journal = "Decision Support Systems ",
volume = "51",
number = "4",
pages = "810 - 820",
year = "2011",
note = "Recent Advances in Data, Text, and Media Mining & Information Issues in Supply Chain and in Service System Design ",
issn = "0167-9236",
doi = "",
url = "",
author = "Sérgio Francisco da Silva and Marcela Xavier Ribeiro and João do E.S. Batista Neto and Caetano Traina-Jr. and Agma J.M. Traina",
keywords = "Feature selection",
keywords = "Genetic algorithms",
keywords = "Ranking quality",
keywords = "Medical image retrieval "

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