Detecting lung cancer from a CT scan.

A convolutional neural network — trained by transfer learning — that reads a chest-CT slice, separates cancer from healthy tissue, and identifies the tumour subtype. Then it shows you exactly where it looked.

A study by Azizbek Okbutayev & Nozima Sotiboldiyeva

Test the model Read the results

—: cancer sensitivity
—: specificity
—: macro AUC

The data

Real chest-CT slices across four diagnoses. The training set is balanced; the test set is left realistically imbalanced.

—training images

—validation

—held-out test

4diagnostic classes

Representative chest-CT slices for each class. — Representative slices, two per class. Source: Chest CT-Scan images (HF mirror `dorsar/lung-cancer`, MIT-licensed; originally Kaggle `mohamedhanyyy`).

The method

Transfer learning lets a small dataset go a long way — we reuse features a network already learned from millions of images.

Preprocess
Resize each slice to 224×224, normalise with ImageNet statistics, and augment the training set (flips, rotation, jitter) to resist overfitting.
Backbone
Take MobileNetV2 pretrained on ImageNet and freeze its convolutional feature extractor.
New head
Train a fresh dropout + linear classifier for the four CT classes with Adam and cross-entropy.
Export
Convert to ONNX so the model runs in your browser — verified to match PyTorch to ~1e-5.

The results

The model is near-perfect at the screening question — is there cancer at all? — and more modest at telling the three cancer subtypes apart, which overlap heavily on a single slice.

—4-class accuracy

—macro F1

—macro AUC

—sensitivity · cancer caught

—specificity · normal cleared

Confusion matrix

Rows = true class · columns = predicted. The diagonal is correct.

Per-class performance

Precision, recall and F1 for each diagnosis.

Training and validation loss and accuracy. — Training history — loss and accuracy per epoch.

One-vs-rest ROC curves. — One-vs-rest ROC curves with per-class AUC.

Sample test predictions. — Sample test predictions — green = correct, red = mistake.

Where it looks

A model can be right for the wrong reasons. Grad-CAM overlays the regions of the scan that most drove each prediction — warmer means more influential.

Grad-CAM heatmaps overlaid on CT scans. — Grad-CAM heatmaps for correctly-classified cancer cases.

Test the model

Drop in a chest-CT slice — or pick a sample. Everything runs locally in your browser via ONNX Runtime Web. No upload, no server.

Download model Download notebook

Drop a CT image
or click to browse

Samples

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Results appear here once you analyse a scan.

The authors