Home▸AI & Machine Learning▸How a CNN Learns to Spot a Tumour on a Brain MRI Scan

🧠 How a CNN Learns to Spot a Tumour on a Brain MRI Scan

Watch a convolutional neural network scan a synthetic brain MRI slice layer by layer, then tune tumour size, scan noise and the decision threshold to see how recall trades off against false alarms.

AI & Machine Learning3DAdvanced60 FPS

A synthetic brain MRI slice flows through two computed convolution-and-pool layers into a live probability gauge, so you can see how a binary tumour classifier turns pixels into a triage decision.

🔬 What It Demonstrates

Each feature-map grid is computed from the actual synthetic scan using edge/blob-style filters and max-pooling, so brighter cells genuinely mark where the network finds tumour-like structure, not a scripted animation.

🎮 How to Use

Pick a tumour or healthy scan, adjust tumour size and scan noise, then set the decision threshold. Run batches of scans and watch recall, precision and accuracy update — notice how a stricter threshold protects precision but can cost recall.

💡 Did You Know?

Because most scans in a screening population are healthy, a model can post high "accuracy" while quietly missing many true tumours — which is why NHS-style triage tools are tuned around recall (sensitivity), not accuracy alone.

⚙ Under the hood

Watch a convolutional neural network scan a synthetic brain MRI slice layer by layer, then tune tumour size, scan noise and the decision threshold to see how recall trades off against false alarms.

artificial intelligenceneural networksconvolutional neural networksmedical imagingbrain scanstumorsmachine learningThree.js

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