Attention ResNet Coupled Convolutional Neural Network Model for Breast Cancer Detection using Mammographic Images
Keywords:
Breast cancer detection, Attention module, Mammography images, ResNet50, and Convolutional Neural Network.Abstract
All over the world, most of the people are affected by breast cancer which results in a high mortality rate. The mortality rate is reduced by early diagnosis, which improves the patients survival rate. The detection of breast cancer at early stage is analyzed by imaging methods as ultrasound and mammographic images. In general, the most common method to detect breast cancer is mammogram analysis, which is a time-consuming process. Several researches diagnosed breast cancer with various analyses, but prior methods need more improvement for better diagnosis. In this, the A2Res-CNN model is utilized for automatically detecting breast cancer with better accuracy. The model trained the extracted features by minimizing the classification loss through the attention mechanism. In this proposed method, the feature extraction is carried out with more efficient A2Res-50. The A2Res-50 enhances the model performance ability with better breast cancer detection. The A2Res-CNN model achieves evaluation metrics of accuracy 96%, sensitivity 97%, and specificity 95%.
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