A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification
<|reference_start|>A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification: 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.<|reference_end|>
@article{ahmadi2024a,
    title={A lightweight deep learning pipeline with DRDA-Net and MobileNet for
  breast cancer classification},
    author={Mahdie Ahmadi, Nader Karimi, Shadrokh Samavi},
    journal={arXiv preprint arXiv:2403.11135},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11135},
    primaryClass={eess.IV cs.CV}
}