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

TL;DR

This paper introduces a CNN-based method for breast cancer image classification that automatically extracts and fuses features from different neural network structures, achieving higher accuracy than traditional manual methods.

Contribution

It proposes a novel feature fusion CNN approach for breast cancer detection that automates feature extraction and improves classification accuracy.

Findings

Classification accuracy reached 89%.

Significant improvement over traditional methods.

Automated feature extraction reduces manual effort.

Abstract

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.