Detection of fibrosis in liver biopsy images using multi-objective genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Thong-on:2017:ICITEE,

• author = "Purit Thong-on and Ukrit Watchareeruetai",
• booktitle = "2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)",
• title = "Detection of fibrosis in liver biopsy images using multi-objective genetic programming",
• year = "2017",
• abstract = "This paper proposes an automatic construction of feature extractor for liver fibrosis detection using a multiobjective genetic programming approach in which a constructed feature extractor was measured in different aspects in which becomes the objectives of the evolutionary run. The result of the evolutionary run is a set of solutions with different strengths and weaknesses. A solution from each experiment is selected and compared with a benchmark hand craft method in by each experiment and top-five manners. One of the best result obtained has 2.09 fibrosis estimation error which is less than the benchmark method with 2.63 fibrosis estimation error.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICITEED.2017.8250486",
• month = oct,
• notes = "Also known as \cite{8250486}",

}

Genetic Programming entries for Purit Thong-on Ukrit WatchAreeruetai

Citations