  Accurate and early detection of breast cancer is essential for successful
treatment. This paper introduces a novel deep-learning approach for improved
breast cancer classification in histopathological images, a crucial step in
diagnosis. Our method hinges on the Dense Residual Dual-Shuffle Attention
Network (DRDA-Net), inspired by ShuffleNet's efficient architecture. DRDA-Net
achieves exceptional accuracy across various magnification levels on the
BreaKHis dataset, a breast cancer histopathology analysis benchmark. However,
for real-world deployment, computational efficiency is paramount. We integrate
a pre-trained MobileNet model renowned for its lightweight design to address
computational. MobileNet ensures fast execution even on devices with limited
resources without sacrificing performance. This combined approach offers a
promising solution for accurate breast cancer diagnosis, paving the way for
faster and more accessible screening procedures.
