Open AccessMammograms Classification Using ELM Based on Improved Sunflower Optimization Algorithm
Author(s) -
Yeheng Sun
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1739/1/012047
Subject(s) - overfitting , computer science , artificial intelligence , extreme learning machine , robustness (evolution) , algorithm , machine learning , pattern recognition (psychology) , optimization algorithm , data mining , mathematics , mathematical optimization , artificial neural network , gene , biochemistry , chemistry
To assist specialists in detecting breast cancer on mammograms with better accuracy and less time consuming, this paper proposes an approach based on improved sunflower optimization algorithm (ISFO) and extreme learning machine (ELM). Firstly, features were extracted by using lifting scheme and gray-level co-occurrence matrix (GLCM). Then, the parameters of ELM were optimized by (ISFO) to obtain the final classification results. Finally, in order to avoid overfitting, the proposed model’s performance was evaluated with k-fold random stratified cross validation, and the experiments compared the model with other models on MIAS datasets. The experimental results show that the proposed model has higher classification accuracy, shorter learning time and stronger robustness on mammograms classification task. Thus, this method could be a promising application in bio-medical and provide a basis for the early diagnosis of breast cancer.