A Review on Automatic Classification of Breast Cancer Using Supervised Learning Strategies

Breast cancer is the most perilous disease affecting women throughout the world from generations. Modern methodologies and developing approaches are focused on complete and confined rehabilitation of breast malignancy. Complete treatment of breast cancer is possible if it can be identified in the early stages of diagnosis as there is no indication of breast cancer in about 90% of cases. To improve this factor, medical image classification is used in recent years, where tissue images are classified into cancerous and non-cancerous. Many researchers have proposed different approaches to achieve better recognition of breast cancer in initial stages with a very low error rate using medical image classification. These methodologies are studied in this paper to gain state-of-the-art. It is observed that different machine learning and deep learning-based breast cancer classification techniques are widely proposed by researchers. These techniques are effective but still have a scope of improvement.