Breast Cancer Diagnosis based on Joint Variable Selection and Constructive Deep Neural Network

Breast cancer is the second most common cancer (after lung cancer) that affect women both in the developed and less developed countries. Nowadays, using the Computer Aided Diagnosis (CAD) techniques becomes a necessity for several reasons: assisting and improving physicians, speed in data processing, harmonization and aid of diagnosis, better access to advanced online-medicine. Recently, several works about Breast Cancer Computer Aided Diagnosis (BC-CAD) have been published, and Neural Networks techniques, particularly deep architectures represent a significant part of these works. In this paper, we prpose a BC-CAD based on joint variable selection and a Constructive Deep Neural Network "ConstDeepNet". A feature variable selection method is applied to decrease the number of inputs used to train a Deep Learning Neural Network. Experiments have been conducted on two datasets, the Wisconsin Breast Cancer Dataset (WBCD) and real data from the north hospital of Belfort (France) to predict the recurrence score of the Oncotype DX. Consequently, the use of joint variable algorithm with ConstDeepNet outperforms the use of the Deep Learning arechitecture alone.