Constructive Deep Neural Network for Breast Cancer Diagnosis. Issue 27 (2018)

• Record Type: Journal Article
• Authors: Zemouri, R.; Omri, N.; Morello, B.; Devalland, C.; Arnould, L.; Zerhouni, N.; Fnaiech, F.
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 27(2018)
• Page Start: 98
• Page End: 103
• Publication Date: 2018
• Subjects: growing algorithm -- deep learning -- neural networks -- breast cancer -- recurrence score -- oncotype DX; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.11.660
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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 "one against all" 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 Trévenans County Hospital located respectively in Dijon and Belfort in France.