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abstract

A proposal for automatic diagnosis of malaria: extended abstract

Published: 13 May 2013 Publication History

Abstract

This paper presents a methodology for automatic diagnosis of malaria using computer vision techniques combined with artificial intelligence. We had obtained an accuracy rate of 74% in the detection system.

References

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A. C. Firmo. Classificação adaboost com treinamento por enxame de partículas para diagnóstico da esquistossomose mansônica no litoral de pernambuco. Master's thesis, Universidade de Pernambuco, Recife, 2010.
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G. Bradski and A. Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, 2008.
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L. S. Garcia. Malaria. Clinics in Laboratory Medicine, 30(1):93--129, 2010. Emerging Pathogens.
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R. González and R. Woods. Digital Image Processing. Prentice Hall, 2008.
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S. Mavandadi, S. Dimitrov, S. Feng, F. Yu, U. Sikora, O. Yaglidere, S. Padmanabhan, K. Nielsen, and A. Ozcan. Distributed medical image analysis and diagnosis through crowd-sourced games: A malaria case study. PLoS ONE, 7(5):e37245, 05 2012.
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W. H. Organization. Malaria, 2012.
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P. Viola and M. J. Jones. Robust real-time face detection. Int. J. Comput. Vision, 57(2):137--154, May 2004.

Cited By

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  • (2017)The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria DiagnosisJMIR Research Protocols10.2196/resprot.67586:4(e70)Online publication date: 25-Apr-2017

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Published In

cover image ACM Other conferences
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
May 2013
1636 pages
ISBN:9781450320382
DOI:10.1145/2487788
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

  • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
  • CGIBR: Comite Gestor da Internet no Brazil

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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Author Tags

  1. artificial intelligence
  2. automatic diagnostic
  3. computer vision techniques
  4. detecting malaria
  5. haar
  6. malaria

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  • Abstract

Conference

WWW '13
Sponsor:
  • NICBR
  • CGIBR
WWW '13: 22nd International World Wide Web Conference
May 13 - 17, 2013
Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2017)The Malaria System MicroApp: A New, Mobile Device-Based Tool for Malaria DiagnosisJMIR Research Protocols10.2196/resprot.67586:4(e70)Online publication date: 25-Apr-2017

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