Breast cancer classification using deep belief networks

• We present a CAD scheme using DBN unsupervised path followed by NN supervised path.
• Our two-phase method ‘DBN-NN’ classification accuracy is higher than using one phase.
• Overall accuracy of DBN-NN reaches 99.68% with 100% sensitivity & 99.47% specificity.
• DBN-NN was tested on the Wisconsin Breast Cancer Dataset (WBCD).
• DBN-NN results show classifier performance improvements over previous studies.

Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN). Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD). The classifier complex gives an accuracy of 99.68% indicating promising results over previously-published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train-test partitions.