Crafted with EdrawMax, this template illustrates a neural network pipeline for medical images (512 x 512 input). It includes four stages: Stage 1 starts with a 7×7 conv and max pool; subsequent stages (2–4) use 1×1 and 3×3 convolutions, batch normalization (BN), and ReLU activation. Each stage progressively increases channel depth (64→256→512→1024), with operations like downsampling (/2). Ideal for deep - learning applications in medical imaging, it showcases how CNN architectures process anatomical data, aiding researchers or developers in designing or understanding image - analysis models.
