[Zem18b] Constructive Deep Neural Network for Breast Cancer Diagnosis

Conférence Internationale avec comité de lecture : 10th IFAC Symposium on Biological and Medical Systems BMS 2018, December 2018, Vol. 51, pp.98 - 103, Brazil, (DOI: 10.1016/j.ifacol.2018.11.660)

Auteurs: R. Zemouri

Mots clés: breast cancer, Constructive Deep Neural Network

Résumé: The Oncotype DX (ODX) breast cancer assay is the worldwide most common and used Gene Expression Profiling (GEP) test. This ODX assay has a great impact on Adjuvant ChemoTherapy (ACT) decision. However, many standard approaches have been proposed and suggested to practitioners. The accuracy of such methods never reached the highest level. This paper deals with the Breast Cancer Computer Aided Diagnosis (BC-CAD) based on a Deep Constructive Neural Network used for the Recurrence Score (RS) prediction of the ODX assay. The proposed ConstDeepNet algorithm was tested to build two classifiers. In the first architecture, a â€one against allâ€ structure is used where one Deep Neural Network is built for each class. In the second architecture, one DNN is used for the three classes. The proposed BC-CAD algorithm is tested on a real data-set and exhibits good performance. The study data set contains 92 cases carcinoma mammary luminal B with available Oncotype DX test results from 2012 to 2017 taken from the Georges Francois Leclerc cancer centre and the North TrÃ©venans County Hospital located respectively in Dijon and Belfort in France.

BibTeX

@inproceedings {
	Zem18b,
	title	=	"{Constructive Deep Neural Network for Breast Cancer Diagnosis}",
	author	=	" R. Zemouri ",
	booktitle	=	"{10th IFAC Symposium on Biological and Medical Systems BMS 2018}",
	year	=	2018,
	month	=	"December",
	volume	=	51,
	pages	=	"98 - 103",
	address	=	" Brazil",
	doi	=	"10.1016/j.ifacol.2018.11.660",
}