Genetic programming for tuberculosis screening from raw X-ray images

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Burks:2018:GECCO,

• author = "Armand R. Burks and William F. Punch",
• title = "Genetic programming for tuberculosis screening from raw {X-ray} images",
• booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference",
• year = "2018",
• editor = "Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre",
• isbn13 = "978-1-4503-5618-3",
• pages = "1214--1221",
• address = "Kyoto, Japan",
• DOI = "doi:10.1145/3205455.3205461",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming",
• abstract = "Genetic programming has been successfully applied to several real-world problem domains. One such application area is image classification, wherein genetic programming has been used for a variety of problems such as breast cancer detection, face detection, and pedestrian detection, to name a few. We present the use of genetic programming for detecting active tuberculosis in raw X-ray images. Our results demonstrate that genetic programming evolves classifiers that achieve promising accuracy results compared to that of traditional image classification techniques. Our classifiers do not require pre-processing, segmentation, or feature extraction beforehand. Furthermore, our evolved classifiers process a raw X-ray image and return a classification orders of magnitude faster than the reported times for traditional techniques.",
• notes = "Also known as \cite{3205461} GECCO-2018 A Recombination of the 27th International Conference on Genetic Algorithms (ICGA-2018) and the 23rd Annual Genetic Programming Conference (GP-2018)",

}

Genetic Programming entries for Armand R Burks William F Punch

Citations