Abstract
Attention mechanism is one of the foremost later and effective deep learning methods which have shown tremendous performance in detection and classification tasks, including medical imaging. Breast cancer is a primary cause of women's cancer-related morbidity and mortality globally; thus, early identification of this malignancy will help to reduce the number of fatalities. The accurate classification of a mild and deadly cancer in microscopic breast images can give an efficient and relatively low-cost technique for breast cancer early detection. This paper proposes a deep learning model based on an attention mechanism. The proposed attention mechanism derives its input features from pre-trained models and passes its output through a Multilinear perceptron and SoftMax for classification. We trained all models on the ICIAR2018 Grand challenge Breast Cancer dataset and compared them with the pre-trained and ensemble models. For quantitative analysis, validation tests were conducted using the performance metrics for each approach. The suggested approach is proven successful, with classification results improving by +1-6%, potentially reducing human errors in the diagnosis process. Furthermore, the proposed method outperforms the state-of-the-art accuracy, with a +3-8% improvement in performance results.