DOI: 10.4018/IJHISI.2018040102 International Journal of Healthcare Information Systems and Informatics Volume 13 • Issue 2 • April-June 2018 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 13 Medical Image Retrieval in Healthcare Social Networks Riadh Bouslimi, ISG, Tunis, Tunisia Mouhamed Gaith Ayadi, ISG, Tunis, Tunisia Jalel Akaichi, ISG, Tunis, Tunisia ABSTRACT In this article, the authors present a multimodal research model to research medical images based on multimedia information that is extracted from a radiological collaborative social network. The opinions shared on a medical image in a medico-social network is a textual description which in most cases requires cleaning by using a medical thesaurus. In addition, they describe the textual description and medical image in a TF-IDF weight vector using a “bag-of-words” approach. The authors then use latent semantic analysis to establish relationships between textual terms and visual terms in shared opinions on the medical image. The model is evaluated against the ImageCLEFmedbaseline, which is the ground truth for the experiments. The authors have conducted numerous experiments with different descriptors and many combinations of modalities. The analysis of results shows that when the model is based on two methods it can increase the performance of a research system based on a single modality both visually or textually. KeywoRdS Bag-of-Word, Latent Semantic Analysis, Medical Image Retrieval, Medical Social Network, Multimodal Fusion 1. INTRodUCTIoN The explosion of medical information in the last 10 years over the Internet has made information seeking for both textual and visual objects a very hot topic of research. In the medical domain, in particular, the vast volumes of visual information produced every day in hospitals in connection with the existence of digital Picture Archiving and Communications Systems (PACS) make the need imperative for advanced ways of searching, i.e., by moving beyond conventional textbased searching towards combining both text and visual features in search queries. Indeed, biomedical information comes in several forms: as text in scientific articles, social networks, as images or illustrations from databases and Electronic Health Records (EHR). Although many methods and tools have been developed, still, we are far from an effective solutionespecially in the case of image retrieval from large and heterogeneous databases. One way towards the improvement of current retrieval facility is data fusion. Data fusion is generally defined as the use of techniques that combines data from multiple sources and gather that information in order to achieve inferences, which will be more efficient and accurate than if they are achieved by means of a single source.