I. Introduction
Breast cancer is the most well-known disease among ladies around the world, representing roughly 30% of all malignant growth cases among ladies. Early detection and diagnosis are crucial for successful treatment and improved outcomes. Mammography is the gold standard for breast cancer screening, and it has been shown to reduce breast cancer mortality rates by up to 30% [1]. However, mammography interpretation can be challenging and time-consuming, and radiologists' performance can vary widely. Therefore, there is a need for automated methods to assist radiologists in detecting breast cancer in its early stages.