AUTHOR=Li Jia , Shi Jingwen , Chen Jianrong , Du Ziqi , Huang Li 

TITLE=Self-attention random forest for breast cancer image classification

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1043463

DOI=10.3389/fonc.2023.1043463

ISSN=2234-943X

ABSTRACT=Early screening and diagnosis of breast cancer can not only detect hidden diseases in time, but also effectively improve the survival rate of patients. Therefore, the accurate classification of breast cancer images becomes the key to auxiliary diagnosis. In this paper, on the basis of extracting multi-scale fusion features of breast cancer images using pyramid gray level co-occurrence matrix, we present a Self-Attention Random Forest (SARF) model as a classifier to explain the importance of fusion features, and can perform adaptive refinement processing on features, thus, the classification accuracy can be improved. In addition, we use GridSearchCV technique to optimize the hyperparameters of the model, which greatly avoids the limitation of artificially selected parameters. Finally, to demonstrate the effectiveness of our method, we perform validation on the breast cancer histopathological image-BreaKHis and breast mammorgraphic image-MIAS datasets. The proposed method achieves an average accuracy of 92.96% and a micro average AUC value of 0.9588 for eight-class classification, and an average accuracy of 97.16% and an AUC value of 0.9713 for binary classification on BreaKHis dataset. Furthermore, an excellent average classification accuracy is 98.79% on MIAS dataset. Compared to other state-of-the-art methods, the experimental results demonstrate that the performance of the proposed method is superior to that of others.