Comparison of Attention Mechanism in Convolutional Neural Networks for Binary Classification of Breast Cancer Histopathological Images

Abstract

AbstractThe quality of classification is crucial in medical applications. Especially when it comes to confirm that the patient does not have a malignant tumor. An example of such an application is a binary classification of breast tumor malignancy based on histopathological images. This paper explains the most popular attention mechanism in convolution neural networks as follows. Convolutional Block Attention Module, Attention Augmented Convolution, and Attention Guided Convolutional Neural Networks. Four neural networks are built and compared. Each is evaluated in the classification problem of histopathological images of breast cancer. On the basis of the results, it is clear that some attentional neural networks can outperform standard convolutional networks in the classification of breast cancer. In our investigation, the convolution networks reached an accuracy level of 90% and an AUC-ROC of 95.9%. It is worse compared to the Convolutional Block Attention Module Network (accuracy 90.7%, AUC-ROC 96.9%) and the Attention-Guided Convolutional Network (accuracy 91.2%, AUC-ROC 96.6%). Attention-augmented convolution remains behind the standard convolutional network, achieving 88.9% accuracy and 94.8% AUC-ROC. The Attention-Guided Convolution Network was the best network of all four. We also compared precision, NPV, sensitivity, specificity, and \(F_{1}\)-score. We came to the conclusion that the Convolutional Block Attention Module network has the highest NPV (90.8%) and sensitivity (96.2%), while the Attention-Guided Convolutional Neural Network scored the highest in precision (92.4%) and specificity (82.9%).KeywordsConvolutional neural networksAttentional neural networksBreast cancerHistopathologic imagesConvolutional block attention moduleAttention-augmented convolutionAttention-guided convolutional neural network