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Having Fun?: Personalized Activity-Based Mood Prediction in Social Media Read chapter Read first chapter

2017 | OriginalPaper | Chapter

2. Automatic Medical Image Multilingual Indexation Through a Medical Social Network

Authors : Mouhamed Gaith Ayadi, Riadh Bouslimi, Jalel Akaichi, Hana Hedhli

Published in: Prediction and Inference from Social Networks and Social Media

Publisher: Springer International Publishing

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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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Metadata
Title
Automatic Medical Image Multilingual Indexation Through a Medical Social Network
Authors
Mouhamed Gaith Ayadi
Riadh Bouslimi
Jalel Akaichi
Hana Hedhli
Copyright Year
2017
Book
Prediction and Inference from Social Networks and Social Media

Print ISBN: 978-3-319-51048-4

Electronic ISBN: 978-3-319-51049-1

Copyright Year: 2017

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

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