Breast Cancer Detection using Deep Learning Techniques

Abstract

Breast cancer is the most common form of cancer among women and the second most common cancer in the world (an estimated 1 152 161 new cases per year), trailing only lung cancer .The current approach to this disease involves early detection and treatment. This approach in the United States yields an 85% 10-year survival rate. Survival is directly related to stage at diagnosis, as can be seen by a 98% 10- year survival rate for patients with stages 0 and I disease compared with a 65% 10-year survival rate for patients with stage III disease. To improve survival in this disease, more patients need to be identified at an early stage.Therefore, we evaluated existing and emerging technologies used for breast cancer screening and detection to identify areas for potential improvement. The main criteria for a good screening test are accuracy, high sensitivity, ease of use, acceptability to the population being screened (with regard to discomfort and time), and low cost. We can begins by describing commonly used breast cancer detection techniques and then delves into emerging modalities. Several studies addressing breast cancer using Deep learning techniques. Many claim that their algorithms are faster, easier, or more accurate than others . This system is based on thermal image processing and Deep learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this was to optimize the learning algorithm. In this system , we applied the deep neural network technique to select the best features and perfect parameter values of the deep machine learning. The present study proves that deep neural network can automatically find the best model by combining feature preprocessing methods and classification algorithms.