ResNet-18 binary classification architecture processes 224x224x3 input images through a stem convolution, four residual layer groups with identity and projection skip connections, and outputs binary predictions for diseased versus healthy classification. Data flows through progressively deeper feature maps: 64 channels at 56x56, 128 at 28x28, 256 at 14x14, and 512 at 7x7, with batch normalization and ReLU activations at each stage. This proven deep learning pattern enables robust feature extraction while mitigating vanishing gradient problems through skip connections, making it ideal for medical imaging and diagnostic tasks. Fork this diagram on Diagrams.so to customize layer depths, channel counts, or adapt for multi-class classification and AWS SageMaker deployment pipelines. ResNet-18's 18-layer depth balances accuracy and inference latency for production healthcare AI systems.