TY - GEN
T1 - Research on the detection method of breast cancer deep convolutional neural network based on computer aid
AU - Li, Mengfan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/14
Y1 - 2021/4/14
N2 - 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.
AB - 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.
KW - Breast cancer medical image
KW - Breast cancer recognition
KW - Computer-aided
KW - Deep convolutional network
KW - Deep learning network
UR - http://www.scopus.com/inward/record.url?scp=85106192052&partnerID=8YFLogxK
U2 - 10.1109/IPEC51340.2021.9421338
DO - 10.1109/IPEC51340.2021.9421338
M3 - Conference Proceeding
AN - SCOPUS:85106192052
T3 - Proceedings of IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021
SP - 536
EP - 540
BT - Proceedings of IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2021
Y2 - 14 April 2021 through 16 April 2021
ER -
