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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 Verlag, 2018.

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


© 2018, Springer International Publishing AG, part of Springer Nature. 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 process text from social networks, we present a new strategy that analyzes 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 PH, Lima BGC, Crispim FC, Vieira T, Missier P, Fonseca B

Editor(s): Aurélio Campilho, Fakhri Karray, Bart ter Haar Romeny

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 15th International Conference on Image Analysis and Recognition (ICIAR 2018)

Year of Conference: 2018

Pages: 605-610

Online publication date: 06/06/2018

Acceptance date: 02/04/2018

Date deposited: 03/01/2019

ISSN: 0302-9743

Publisher: Springer Verlag


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