%0 Journal Article %A ZHAO Jingxia %A QIAN Yurong %A NAN Fangzhe %A ZHANG Han %A XING Yanni %T Method with CNN Multi-Layer Feature Fusion and ELM Diagnosis for Breast Diseases %D 2020 %R 10.3778/j.issn.1002-8331.1810-0421 %J Computer Engineering and Applications %P 122-127 %V 56 %N 4 %X

Aiming at the problems of low accuracy and long time-consuming in traditional computer-aided diagnosis, a new method of breast disease diagnosis based on Convolutional Neural Networks(CNN) multi-layer feature fusion and Extreme Learning Machine(ELM) is proposed. CNN is used to extract multi-layer features from mammograms, multi-scale pooling is used to fuse the features extracted from each layer, and extreme learning machine classifier is used to diagnose breast diseases quickly. The experimental results show that the average accuracy of the proposed method is 97.13%, and the diagnosis time is 6.43 ms. This method can effectively improve the accuracy of breast disease diagnosis, shorten the diagnosis time, and has good robustness and generalization ability.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1810-0421