Abstract: One in eight women may suffer from breast cancer at some point in their lives, making it a frequent health issue for women. Since radioactive radiation exposure makes breast cancer treatment risky, many women choose to forego getting the disease diagnosed. The non-invasive nature, the danger of radiation, and the specificity of the identification of breast tumors are all problems with breast cancer screening methods. Medical imaging often makes use of deep learning algorithms. This essay seeks to offer a thorough analysis of the benefits and drawbacks of breast cancer screening methods. Deep learning techniques' potential for use in the early identification of breast cancer is being investigated. Investigations are also conducted on the datasets and performance metrics for breast cancer. The directions for breast cancer research in the future are examined. The main goals are to provide an in-depth analysis of this area and to inspire creative researchers.

Keywords: SVM, Machine Learning, Deep Learning, CNN, VGG-16, and Breast Cancer.

| DOI: 10.17148/IARJSET.2022.9718