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

arXiv: https://arxiv.org/abs/2403.11135 · HTML: https://mlsanity.com/paper/2403.11135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 17 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Classification of breast cancer histology images using transfer learning](https://mlsanity.com/paper/1802.09424/) (2018)

## Abstract (arXiv)

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.