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Ontology based decision system for breast cancer diagnosis
Author(s): Soumaya Trabelsi Ben Ameur; Florence Cloppet; Laurent Wendling; Dorra Sellami
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Paper Abstract

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

Paper Details

Date Published: 13 April 2018
PDF: 8 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106962I (13 April 2018); doi: 10.1117/12.2309647
Show Author Affiliations
Soumaya Trabelsi Ben Ameur, Paris Descartes Univ. (France)
Univ. de Sfax (Tunisia)
Florence Cloppet, Paris Descartes Univ. (France)
Laurent Wendling, Paris Descartes Univ. (France)
Dorra Sellami, Univ. de Sfax (Tunisia)

Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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