A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification

arXiv:2403.11135v1 Announce Type: cross
Abstract: 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 …

abstract arxiv attention cancer classification cs.cv deep learning detection diagnosis eess.iv images mobilenet network novel paper pipeline residual treatment type