# Research on the Detection Method of Breast Cancer Deep Convolutional Neural Network Based on Computer Aid

arXiv: https://arxiv.org/abs/2104.11551 · HTML: https://mlsanity.com/paper/2104.11551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 23 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network](https://mlsanity.com/paper/1811.04241/) (2018)

## Built on by (most cited first, 1 of 1)

- [ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification](https://mlsanity.com/paper/2211.00749/) (2022)

## Abstract (arXiv)

Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and labor-intensive and difficulty in extracting high-quality features. Therefore, the paper proposes a computer-based feature fusion Convolutional neural network breast cancer image classification and detection method. The paper pre-trains two convolutional neural networks with different structures, and then uses the convolutional neural network to automatically extract the characteristics of features, fuse the features extracted from the two structures, and finally use the classifier classifies the fused features. The experimental results show that the accuracy of this method in the classification of breast cancer image data sets is 89%, and the classification accuracy of breast cancer images is significantly improved compared with traditional methods.