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Automatic Medical Image Multilingual Indexation Through a Medical Social Network

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Prediction and Inference from Social Networks and Social Media

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

Medical social networking sites enabled multimedia content sharing in large volumes, by allowing physicians and patients to upload their medical images. These images are diagnosed and commented, in different languages, by several specialists instantly. Moreover, it is necessary to employ new techniques, in order to automatically extract information and analyze knowledge from the huge number of comments expressing specialist’s analyzes and recommendations. For this reason, we propose a terms-based method in order to extract the relevant terms and words which can describe the medical image. Furthermore, significant extracted terms and keywords will be used later to index medical images, in order to facilitate their search through the social network site. In fact, we need to take account, in our work, that existing comments are expressed in different languages. So, it is essential to implement a multilingual indexation method to eliminate the ambiguity which will be the cause of the effectiveness’s reduction of the search function. In order to palliate this situation, we propose a multilingual mixed approach which concentrates on algorithms based on statistical methods and external multilingual semantic resources, in order to handle and to cover different languages. The use of external resources, such as semantic multilingual thesaurus, can improve the efficiency of the indexing process. Our proposed method can be applied in different languages. It is also essential to implement an auto-correction of the medical terms by using a medical dictionary. The correction of terms helps to eliminate the ambiguity which will be the cause of the reduction in the frequency of appearance of such terms. The correction of terms has taken into consideration that terms are presented in different languages. Our study is validated by a set of experiments and a comparison study with some existing approaches in literature. Experimental results have indicated that the proposed system has a superior performance compared to other systems, which is satisfactory.

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Notes

  1. 1.

    http://iom.nationalacademies.org/

  2. 2.

    http://corp.yougov.com/healthcare/

  3. 3.

    http://www.sobercircle.com

  4. 4.

    http://www.sparkpeople.com/

  5. 5.

    https://www.fitocracy.com/

  6. 6.

    https://www.dacadoo.com/

  7. 7.

    http://www.asklepios.com/

  8. 8.

    http://www.acc.org/

  9. 9.

    http://www.diabspace.com/

  10. 10.

    http://www.parlonscancer.ca/

  11. 11.

    http://www.renaloo.com/

  12. 12.

    http://www.rxspace.com/

  13. 13.

    https://metamap.nlm.nih.gov/

  14. 14.

    http://ai.stanford.edu/~rion/parsing/minipar_viz.html

  15. 15.

    http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/

  16. 16.

    http://www.lexilogos.com/medical_dictionnaire.htm

  17. 17.

    http://www.nzdl.org/Kea/download.html

  18. 18.

    http://perso.univ-lyon2.fr/~maniezf/Corpus/Corpus_medical_FR_CRTT.htm

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Author information

Authors and Affiliations

  1. Department of Computer Sciences, ISG, BESTMOD, Tunis, Tunisia

    Mouhamed Gaith Ayadi, Riadh Bouslimi & Jalel Akaichi

  2. Emergency Department, Charles Nicolle Hospital, Tunis El Manar University, Tunis, Tunisia

    Hana Hedhli

Authors
  1. Mouhamed Gaith Ayadi
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  2. Riadh Bouslimi
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  3. Jalel Akaichi
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  4. Hana Hedhli
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Corresponding author

Correspondence to Mouhamed Gaith Ayadi .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Jalal Kawash

  2. Information Science Department, University of Arkansas at Little Rock, Little Rock, Arkansas, USA

    Nitin Agarwal

  3. Department of Computer Engineering, TOBB University, Ankara, Turkey

    Tansel Özyer

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Ayadi, M.G., Bouslimi, R., Akaichi, J., Hedhli, H. (2017). Automatic Medical Image Multilingual Indexation Through a Medical Social Network. In: Kawash, J., Agarwal, N., Özyer, T. (eds) Prediction and Inference from Social Networks and Social Media. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-51049-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-51049-1_2

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  • Online ISBN: 978-3-319-51049-1

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