Classification of Breast Cancer Tumors from Histopathological Images through a Modified ResNet-50 Architecture - Figure 1. The architecture of our proposed method
Classification of Breast Cancer Tumors from Histopathological Images through a Modified ResNet-50 Architecture - Figure 1. The architecture of our proposed method
We propose a supervised learning approach based on a slightly modified ResNet-50 (He et al., 2016) architecture, which can be observed in Figure 1. We add a sequential part at the end of the ResNet50 architecture which comprises an extra Batch Normalization Layer in front of a Fully Connected layer with 256 elements, using a ReLu activation function. Then the output of these layers is reduced to just one, since we are only interested in classifying if cancer is present or not, for which we use a Sigmoid activation function.
https://brain.edusoft.ro/index.php/brain/article/view/1598
- University of Bacău Romania
breast cancer, ResNet-50, image analysis, histopathology, deep learning, image classification
breast cancer, ResNet-50, image analysis, histopathology, deep learning, image classification
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