ResNet-18 binary classification architecture with residual blocks for medical image analysis on Azure. Input images (224×224 RGB) flow through Conv1, MaxPool, and three residual block layers (64, 128, 256 filters) with identity and projection skip connections, batch normalization, and ReLU activations. Global average pooling and a fully connected layer (512→2) output softmax probabilities for patient (positive) vs. healthy (negative) classification. This deep residual network reduces vanishing gradient problems while maintaining computational efficiency, ideal for Azure ML inference pipelines requiring high accuracy on medical imaging tasks. Fork and customize this diagram on Diagrams.so to document your ResNet-18 deployment, adjust filter counts, or integrate Azure Container Instances or Batch endpoints.