EfficientNet-Based Deep Learning Approach for Breast Cancer Detection With Mammography Images

Abstract

Breast cancer is a major health concern affecting women worldwide. Early detection and accurate diagnosis of breast cancer are crucial for improving patient outcomes. In recent years, deep learning techniques have been increasingly applied to medical imaging, including mammography, for the detection and diagnosis of breast cancer. In this study, we proposed a deep learning-based approach using the EfficientNet architecture for the detection and classification of breast cancer. We evaluated the performance of our proposed approach using mammography images from the CBIS-DDSM dataset and achieved accuracy of 0.75 and AUC of 0.83. Our results demonstrate the effectiveness of using deep learning techniques in medical imaging for breast cancer detection and diagnosis.