Optimizing Transfer Learning for Low-Resource Breast Imaging: A Comparative Study of Progressive Layer Unfreezing and Pre-trained Backbones
Objective: To find the optimal combination of pre-trained convolutional neural network (CNN) architectures and depth of progressive layer unfreezing for breast cancer classification in a low-resource mammography scenario through transfer-learning. Methods: Five pre-trained CNN backbones (VGG-19, ResNet50, DenseNet121,...