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Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus

Lookup NU author(s): Professor Paolo MissierORCiD



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer, 2018.

For re-use rights please refer to the publisher's terms and conditions.


Zika and Dengue are viral diseases transmitted by infected mosquitoes (Aedes aegypti) found in warm, humid environments. Mining data from social networks helps to find locations with highest density of reported cases. Differently from approaches that analyze text from social networks, we present a new strategy analyzing Instagram images. We use two customized Deep Neural Networks. The first detects objects commonly used for mosquito reproduction with 85% precision. The second differentiates mosquitoes as Culex or Aedes aegypti with 82.5% accuracy. Results indicate that both networks can improve the effectiveness of current social network mining strategies such as the VazaZika project.

Publication metadata

Author(s): Barros HP, Lima BGC, Crispim FC, Vieira T, Missier P, Fonseca B

Editor(s): Campilho A; Karray F: ter Haar Romeny B

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 15th International Conference on Image Analysis and Recognition

Year of Conference: 2018

Pages: 605-610

Print publication date: 31/07/2018

Online publication date: 06/06/2018

Acceptance date: 02/04/2018

Date deposited: 08/07/2018

ISSN: 0302-9743

Publisher: Springer


DOI: 10.1007/978-3-319-93000-8_69


Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783319929996