BREAST CANCER SEVERITY DEGREE PREDICATION USING DEEP LEARNING TECHNIQUES

• Main Authors: Alaa M. El halees, Mohammed H. Tafish
• Format: Article
• Language: English
• Published: Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) 2020-03-01
• Series: Jordanian Journal of Computers and Information Technology
• Subjects: breast cancer severity; medical data; deep learning; deep neural network; recurrent neural network; deep boltzmann machines
• Online Access: http://jjcit.org/Volume%2006,%20Number%2001/7-DOI%2010.5455-jjcit.71-1568230142.pdf
• ISSN: 2413-9351; 2415-1076

Breast cancer is one of the most common types of cancer most often affecting women. It is a leading cause of cancer death in less developed countries. Thus, it is important to characterize the severity of the disease as soon as possible. In this paper, we applied deep learning methods to determine the severity degree of patients with breast cancer, using real data. The aim of this research is to characterize the severity of the disorder in a shorter time compared to the traditional methods. Deep learning methods are used because of their ability to detect target class more accurately than other machine learning methods, especially in the healthcare domain. In our research, several experiments were conducted using three different deep learning methods, which are: Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Deep Boltzmann Machine (DBM). Then, we compared the performance of these methods with that of the traditional neural network method. We found that the f-measure of using the neural network was 74.52% compared to DNN which was 88.46 %, RNN which was 96.79% and DBM which was 97.28%.