Mammograms Classification Using ELM Based on Improved Sunflower Optimization Algorithm

• Published in: Journal of physics. Conference series Vol. 1739; no. 1; pp. 12047 - 12054
• Main Author: Sun, Yeheng
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.01.2021
• Subjects: Algorithms; Artificial neural networks; Breast cancer; Classification; classification model; extreme learning machine (ELM); Feature extraction; improved sunflower optimization algorithm ((ISFO)); lifting wavelet decomposition (LWD); Machine learning; mammograms; Mammography; Model accuracy; Model testing; Optimization; Optimization algorithms; Physics; random stratified cross validation (RSCV); ray-level co-occurrence matrix (GLCM); Sunflowers
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1739/1/012047

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.