Abstract
This paper proposes a new approach of automatic medical image annotation since a social network whose users are student doctors in radiology in order to obtain report rapids on medical images. Indeed, the present study suggests a social network of collaboration where students or doctors can share their knowledge. Moreover, the annotations are used in order to extract the relevant keywords as well as the concepts which can describe the medical image. At this level, it is vital to implement an auto-correction of the medical terms by using a medical dictionary to eliminate the ambiguity which will be the cause of the reduction in the frequency of appearance of such terms. More specifically, this study has conducted a comparative study to evaluate the needed approach in order to obtain respectable results.
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The Levenshtein distance is a measure approximate matching of strings (Navarro 2001).
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Bouslimi, R., Akaichi, J. Automatic medical image annotation on social network of physician collaboration. Netw Model Anal Health Inform Bioinforma 4, 10 (2015). https://doi.org/10.1007/s13721-015-0082-5
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DOI: https://doi.org/10.1007/s13721-015-0082-5