Next Article in Journal
InfoFlow: A Distributed Algorithm to Detect Communities According to the Map Equation
Next Article in Special Issue
Optimal Number of Choices in Rating Contexts
Previous Article in Journal
Safe Artificial General Intelligence via Distributed Ledger Technology
Previous Article in Special Issue
Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning
Open AccessArticle

Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects

1
Computers, Imaging, Electronics & Systems (CIELS) from CEM-LAB National Engineering School of Sfax, Tunisia University of Sfax, ENIS, BP 1173, Sfax 3038, Tunisia
2
Intelligent System of Perception (SIP) from LIPADE lab, Paris Descartes University, 45 Saint Péres street, 75006 Paris, France
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Big Data Cogn. Comput. 2019, 3(3), 41; https://doi.org/10.3390/bdcc3030041
Received: 17 May 2019 / Revised: 2 July 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Computational Models of Cognition and Learning)
In this work, we build a computer aided diagnosis (CAD) system of breast cancer for high risk patients considering the breast imaging reporting and data system (BIRADS), mapping main expert concepts and rules. Therefore, a bag of words is built based on the ontology of breast cancer analysis. For a more reliable characterization of the lesion, a feature selection based on Choquet integral is applied aiming at discarding the irrelevant descriptors. Then, a set of well-known machine learning tools are used for semantic annotation to fill the gap between low level knowledge and expert concepts involved in the BIRADS classification. Indeed, expert rules are implicitly modeled using a set of classifiers for severity diagnosis. As a result, the feature selection gives a a better assessment of the lesion and the semantic analysis context offers an attractive frame to include external factors and meta-knowledge, as well as exploiting more than one modality. Accordingly, our CAD system is intended for diagnosis of breast cancer for high risk patients. It has been then validated based on two complementary modalities, MRI and dual energy contrast enhancement mammography (DECEDM), the proposed system leads a correct classification rate of 99%. View Full-Text
Keywords: ontology; breast cancer; semantic analysis; feature selection; semantic gap; MRI; DECEDM ontology; breast cancer; semantic analysis; feature selection; semantic gap; MRI; DECEDM
Show Figures

Figure 1

MDPI and ACS Style

Trabelsi Ben Ameur, S.; Sellami, D.; Wendling, L.; Cloppet, F. Breast Cancer Diagnosis System Based on Semantic Analysis and Choquet Integral Feature Selection for High Risk Subjects. Big Data Cogn. Comput. 2019, 3, 41.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop