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

TL;DR

This paper presents a lightweight deep learning pipeline combining DRDA-Net and MobileNet for accurate and efficient breast cancer classification in histopathological images, suitable for resource-limited devices.

Contribution

It introduces a novel combination of DRDA-Net with MobileNet to enhance accuracy and computational efficiency in breast cancer histopathology analysis.

Findings

Achieves high accuracy across multiple magnification levels

Ensures fast execution on resource-limited devices

Demonstrates effectiveness on the BreaKHis dataset

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 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.