Breast Cancer Detection Using Machine Learning and Its Classification

Abstract

Throughout the world, one of the most frequently occurring cancers is breast cancer. Thousands of women succumb to breast cancer every year. Due to the high fatality rate in humans, breast cancer is a cause for concern. It attracts many researchers and raises concern about current research and development. If breast cancer can be traced early on, it can improve prognosis and enhance the probability of survival. Early diagnosis not only can promote timely treatment for patients, but it also reduces the mortality rate. However, early tracing of the disease has turned into an increasingly serious concern due to the rapid increase in the world population. With rapid population growth, the chances of succumbing to breast cancer are extremely high. Statistically, it comes second on the list of most threatening cancers amongst women all over the world. Hence, precise detection and treatment of this ailment are of prime importance. The classification of patients into malignant or non-malignant groups is the subject of much research. In this direction, machine learning is highly regarded as a serious contender for the detection of breast cancer patterns. Machine learning offers distinct advantages in extracting sensitive information from breast cancer datasets. The information extracted helps in the early detection of this fatal disease. With the help of machine learning, a cancerous lump is traced at an early stage and is further narrowed down to a lump in one breast. The treatment can be started at an early stage to prevent the situation from getting worse and hence, making the treatment more effective. If the cancerous lump has already traversed to more body parts, then the treatment becomes complex and will require targeting all cancerous cells within the body. It’s also been claimed that this form of cancer is the easiest cancer to be cured only if detected at an early stage. This inference leads to the question that if it is easier to cure and the cause of so many deaths worldwide, why hasn’t its detection been ramped up yet? Integrating the concept of machine learning into the detection of cancer comes into play here. With data acquired over the years and highly efficient algorithms around the corner, machine learning can study the patterns and images of X-rays and point out anomalies that could suggest the occurrence of lumps.