Literature review of breast cancer detection using machine learning algorithms

Cancer is the leading cause of non-accidental deaths worldwide. Specifically, nearly 10 million people died globally from cancer in the year 2020. Breast Cancer (BC) is a common and fatal disease among women worldwide, and ranks fourth among the fatal diseases among various cancers, such as cervical, colorectal, and cervical tumors and brain tumors. In addition, the number of new breast cancer patients is expected to increase by 70% in the next 20 years. Therefore, early and accurate diagnosis plays a pivotal role in improving prognosis and increasing the survival rate of cancer patients from 30 to 50%. With technical advances in healthcare, machine learning and deep learning play an important role in processing and analyzing a large number of medical images. The aim of this study is to identify studies that have been done on the application of classification techniques in diagnosing BC and analyze them from four perspectives: classification techniques used, Dataset used, Programming language used and best accuracy. We conducted a systematic literature review of 32 selected studies published between 2002 and 2020. The results showed that among the classification techniques examined, artificial neural networks, support vector machines and k-nearest neighbor were the most widely used. Moreover, artificial neural networks, support vector machines, and group classifiers have been implemented better than other techniques, with average accuracy values between 83.45% and 99.30%. Most of the selected studies used Wisconsin CSV dataset and a few of the studies used different types of images such as mammography, ultrasound, and micro images.