关键词: 多特征融合, 多尺度池化, 极限学习机, 乳腺疾病诊断

Abstract:

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.

Key words: multi-feature?fusion, multi-scale pooling, extreme learning machine, diagnosis of breast diseases