Enhancing Breast Cancer Detection in Mammography Images: A Comprehensive Analysis

Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early detection plays a crucial role in improving patient outcomes, making accurate and efficient breast cancer detection a critical task in mammography screening. In this research, we present a comprehensive analysis of deep learning techniques for breast cancer detection in mammography images. Our approach incorporates various data augmentation techniques, including upsampling, downsampling, vertical flip, and horizontal flip, to enhance the model’s ability to capture diverse patterns and features in mammograms. We evaluate the performance of different deep learning models, namely YOLOX, and ConvNeXtv1-Small, using local cross-validation.