ResNet50 for image classification

The ResNet architecture is designed to ease the difficulty of training deep neural networks by adding the skipping shortcut connections between one layer and a few stacked layers after that layer, to fit a residual mapping, so that the network can avoid getting saturated rapidly and the depth of the network can be increased greatly even to 1,000 layers while maintaining low complexity³³. A few models based on the ResNet architecture (ResNet34, ResNet50, ResNet101, ResNet152) have been tested on the ImageNet dataset⁴⁴, and the ResNet50 model is also used in medical image classification, e.g., detecting glaucomatous discs from retinal photos⁴⁵, with human-like level performance. We used the ResNet50 model as the backbone of the method for image classification. We assigned the slide-level label to every patch and performed patch-level classification. At the patient level, the aggregated class probabilities over all image patches from the same subject were used to classify a case. The ResNet50 model built into the TensorFlow package was adopted in this study for image classification. In this study, the ImageNet pre-trained weights were used to initialize the model. A dropout layer⁴⁶ was added on the output layer before the softmax classification layer to control overfitting. Adam optimizer⁴⁷ was used with a batch size of 16, learning rate of 1×10−5, decay rate of 1×10−6, momentum of 0.9, and 100 epochs.