# Breast Cancer Image Classification Method Based on Deep Transfer Learning

Source: [Breast Cancer Image Classification Method Based on Deep Transfer Learning](<https://www.sota2.com/research/paper/68fa74ca1e209131cea63db6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:18:28.201Z

Authors: Weimin Wang, Yufeng Li, Xu Yan, Mingxuan Xiao, Min Gao

Published: 2024-04-14T12:09:47.000Z

## Abstract

To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. This algorithm is based on the DenseNet structure of deep neural networks, and constructs a network model by introducing attention mechanisms, and trains the enhanced dataset using multi-level transfer learning. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0\\% in the test set, with a significantly improved classification accuracy compared to previous models, making it applicable to medical breast cancer detection tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.09226>)

[PDF](<https://arxiv.org/pdf/2404.09226.pdf>)