A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification
This work addresses the problem of accurate and early breast cancer diagnosis for medical applications, offering an incremental improvement through a hybrid method.
The paper tackled breast cancer classification in histopathological images by introducing a deep-learning approach combining DRDA-Net and MobileNet, achieving exceptional accuracy on the BreaKHis dataset while ensuring computational efficiency for real-world deployment.
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.