# Breast Cancer Image Classification Method Based on Deep Transfer Learning

arXiv: https://arxiv.org/abs/2404.09226 · HTML: https://mlsanity.com/paper/2404.09226/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimization](https://mlsanity.com/paper/2403.13703/) (2024)
- [Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features](https://mlsanity.com/paper/2201.07227/) (2022)
- [Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis](https://mlsanity.com/paper/2403.16212/) (2024)

## Built on by (most cited first, 12 of 12)

- [Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks](https://mlsanity.com/paper/2405.11704/) (2024)
- [Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation](https://mlsanity.com/paper/2411.03572/) (2024)
- [Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm](https://mlsanity.com/paper/2410.12259/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study](https://mlsanity.com/paper/2410.20792/) (2024)
- [Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis](https://mlsanity.com/paper/2410.24046/) (2024)
- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [Dynamic Scheduling Strategies for Resource Optimization in Computing Environments](https://mlsanity.com/paper/2412.17301/) (2024)
- [Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models](https://mlsanity.com/paper/2412.19449/) (2024)
- [Multi-Scale Transformer Architecture for Accurate Medical Image Classification](https://mlsanity.com/paper/2502.06243/) (2025)
- [Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data](https://mlsanity.com/paper/2412.20345/) (2024)
- [A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data](https://mlsanity.com/paper/2503.02124/) (2025)

## Abstract (arXiv)

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.