Review: Breast Cancer Detection Using Deep Learning

Abstract

Breast cancer or ductal carcinoma is prevalent in women and is the leading cause of women's death worldwide. Delaying breast cancer or tumor growth has long-term effects, can also become life-threatening. So the tumor should be identified as early as possible to control the growth and preventing to spread to other tissues. Several types of researches have been done to detect breast cancer early so that the treatment can be started to increase the chance of survival. A mammography procedure for early detection and diagnosis of breast cancer is commonly advised. There are so many techniques that are used for malignancy prediction. Using Artificial Intelligence-powered machine learning is widely recommended. Researches are conducted in machine learning to detect cancerous tumors in the human body. Mainly used algorithms which give high accuracy are SVM, naïve bays, decision trees, KNN. Deep learning, the machine learning sub-branch, can also be used to classify breast cancer. Deep learning is a method that is mostly used to clear, rectify, and detect machine learning errors or disadvantages. Convolutional Neural Networks are the perfect deep learning method for overcoming the drawbacks of machine learning in malignancy detection; however, other strategies such as a recurrent neural network and a deep belief network are being used to overcome the shortcomings of machine learning. As a consequence, using deep learning rather than machine learning yields better results. This review paper's primary motivation is to make budding researchers aware that breast cancer is a serious issue among women and we need to be swift in using different technologies to detect and to improve accuracy as efficiently as possible, it is very important to save our mothers, sisters, loved ones and our society from this dangerous predator. In this review paper, we have discussed all the techniques used by different authors.