Home▸AI & Machine Learning▸How Deep Learning Reads a Chest X-Ray for Signs of Pneumonia

🫁 How Deep Learning Reads a Chest X-Ray for Signs of Pneumonia

An interactive 3D walkthrough of a CNN reading a chest X-ray for pneumonia: watch the image funnel through convolutional layers to a probability, then check the decision against a Grad-CAM heatmap.

AI & Machine Learning3DAdvanced60 FPS

Watch a synthetic chest X-ray funnel through the convolutional layers of a CNN toward a pneumonia probability, then check the model's Grad-CAM heatmap to see whether it's really looking at the infiltrate.

🔬 What It Demonstrates

Stacked feature-map layers shrink spatially while growing in channel depth as they move from raw pixels to abstract patterns; the final probability is compared against a decision threshold that trades sensitivity against specificity.

🎮 How to Use

Adjust infiltrate severity and which lung is affected, move the decision threshold, and toggle the Grad-CAM overlay. Run a forward pass to see activation pulse from the X-ray through the network to the output gauge.

💡 Did You Know?

Clinical screening CNNs are usually tuned to favour sensitivity over raw accuracy, because a missed pneumonia case is far costlier than a false alarm that a radiologist double-checks.

⚙ Under the hood

An interactive 3D walkthrough of a CNN reading a chest X-ray for pneumonia: watch the image funnel through convolutional layers to a probability, then check the decision against a Grad-CAM heatmap.

deep learningcnnmedical imagingpneumoniachest x-rayartificial intelligenceimage recognitionThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install