A Study on Computer Aided Techniques for Histopathology Based Breast Cancer Classification

Cancer is regarded to be among the most dangerous illnesses among human beings, because there is no successful treatment. The most prevailing type of cancer among women is breast cancer. According to recent survey, over 276,000 active incidents of invasive breast cancer have been spotted every year. To put these statistics into context, 64% are realized early inside the cancer cycle, providing a 99% probability of survival. Machine Learning (ML) has been implemented successfully in the diagnosis and prognosis of numerous deadly ailments. ML assist in treatment planning as well as therapy, which increases the likelihood of patient's survival. Deep Learning (DL) algorithms have been developed for investigating significant characteristics of disease treatment and diagnosis. Breast cancer are usually identified with the help of mammograms and histopathological images. Histopathological images have been widely utilized technique for detecting breast cancer because genetic study is prohibitively expensive. This study provides a detailed analysis on deep learning and machine learning approaches to perform identification and treatment of breast cancer using histopathological imaging.