Enhanced Mammogram Images Classification Through Comprehensive CNN Parameters Analysis

The rising incidence and death rate of breast cancer make it a serious issue. To tackle this problem, this study has applied convolutional Neural Networks (CNN) for breast cancer detection using mammography images. CNN can automatically learn hierarchical feature representations directly from raw image data. This study evaluates the performance of CNN models in predictive tasks, focusing on the impact of various parameters such as dropout rate, the number of convolutional layers, batch size, and data augmentation techniques. This study uses the mammography images dataset of CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography). The experimental results highlight the critical role of parameter selection in optimizing model performance.