loading
Documents

Research.Publish.Connect.

Paper

Authors: Nada Souissi ; Hajer Ayadi and Mouna Torjmen-Khemakhem

Affiliation: Research Laboratory on Development and Control of Distributed Applications (ReDCAD), Department of Computer Science and Applied Mathematics, National School of Engineers of Sfax, University of Sfax, Tunisia

ISBN: 978-989-758-353-7

Keyword(s): Text-based Image Retrieval, Convolutional Neural Network, Specific Medical Image Features, Word2vec.

Abstract: With the proliferation of digital imaging data in hospitals, the amount of medical images is increasing rapidly. Thus, the need for efficient retrieval systems, to find relevant information from large medical datasets, becomes high. The Convolutional Neural Network (CNN)-based models have been proved to be effective in several areas including, for example, medical image retrieval. Moreover, the Text-Based Image Retrieval (TBIR) was successful in retrieving images with textual description. However, in TBIR, all queries and documents are processed without taking into account the influence of certain medical terminologies (Specific Medical Features (SMF)) on the retrieval performance. In this paper, we propose a re-ranking method using the CNN and the SMF for text-medical image retrieval. First, images (documents) and queries are indexed to specific medical image features. Second, the Word2vec tool is used to construct feature vectors for both documents and queries. These vectors are the n integrated into a neural network process and a matching function is used to re-rank documents obtained initially by a classical retrieval model. To evaluate our approach, several experiments are carried out with Medical ImageCLEF datasets from 2009 to 2012. Results show that our proposed approach significantly enhances image retrieval performance compared to several state of the art models. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.227.233.55

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Souissi, N.; Ayadi, H. and Torjmen-Khemakhem, M. (2019). Text-based Medical Image Retrieval using Convolutional Neural Network and Specific Medical Features.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-353-7, pages 78-87. DOI: 10.5220/0007355400780087

@conference{healthinf19,
author={Nada Souissi. and Hajer Ayadi. and Mouna Torjmen{-}Khemakhem.},
title={Text-based Medical Image Retrieval using Convolutional Neural Network and Specific Medical Features},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2019},
pages={78-87},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007355400780087},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Text-based Medical Image Retrieval using Convolutional Neural Network and Specific Medical Features
SN - 978-989-758-353-7
AU - Souissi, N.
AU - Ayadi, H.
AU - Torjmen-Khemakhem, M.
PY - 2019
SP - 78
EP - 87
DO - 10.5220/0007355400780087

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.