Effective Detection for Invasive Ductal Carcinoma Histopathology Images Based on ResNet

Breast cancer is the second biggest cause of cancer death among women in the United States. The most prevalent subtype of all breast cancers is invasive ductal carcinoma which is invasive ductal carcinoma. Traditional methods are hard to predict this cancer well. In this paper, we proposed a self-defined convolutional neural network (CNN) and employed a typical structure named ResNet for classification. Moreover, we compared the CNN model and the ResNet-50 model's ability in detecting breast cancer. Different parameters e.g. batch size were also changed in terms of different situations to find the difference of the performance. We built 4 sets of experiments for testing the collected datasets by testing the training accuracy and validation accuracy of the two models. The result showed that the accuracy of the ResNet-50 is higher than that of a self-defined CNN. Therefore, the new method should be applied in the process of the detection of breast cancer.