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What the Keras example does
A 2D CNN processes individual images; a 3D CNN applies its filters across all three spatial axes of a volume, so it can learn patterns that extend across adjacent CT slices. As Keras’s example by Hasib Zunair puts it, “A 3D CNN is simply the 3D equivalent: it takes as input a 3D volume or a sequence of 2D frames (e.g. slices in a CT scan), 3D CNNs are a powerful model for learning representations for volumetric data.” Read the Keras 3D image-classification example.
The example uses a subset of MosMedData and maps scans into two groups, normal and abnormal, based on the dataset directories and accompanying radiological findings. Its prediction is a binary class output, not a measure of disease severity or a diagnosis for an individual. The tutorial does not establish external validation, clinical utility, regulatory status, or performance across institutions.
Prepare the CT volumes
Load NIfTI scans and scale intensities
The example loads NIfTI files with Nibabel and reads their voxel values. It treats the values as Hounsfield units (HU), clips them to the range −1000 through 400, then scales the clipped values to floating-point numbers from 0 to 1. This keeps the model input in a bounded range while preserving distinctions within the chosen window.
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Rotate and resize to a common shape
Scans are rotated and interpolated to a spatial shape of 128 × 128 × 64 voxels (width × height × depth). A common shape lets the model process fixed-size batches, but it is a choice made for this tutorial, not a universal CT preprocessing standard. Different scanners, acquisition protocols, orientations, labels, and tasks may require different transforms; validate preprocessing against the data you intend to use.
Split and shape the data
The tutorial selects 200 scans: 100 in each class. It assigns 70 scans from each class to training and 30 from each class to validation, giving 140 training scans and 60 validation scans. The split is balanced by class, but the example does not specify a random seed.
With channels-last layout, each preprocessed scan has shape (128, 128, 64, 1): three spatial dimensions followed by one channel. A batch adds a leading sample dimension, so its tensor shape is (batch_size, 128, 128, 64, 1). Keras’s Conv3D API documentation describes the layer as operating on volumes and documents the five-dimensional batched tensor convention. Confirm the configured data format when adapting the example; channels-first instead places the channel axis before the spatial axes.
Augment training data
The example applies small random rotations to training volumes, while validation volumes receive the channel dimension without random rotation. This helps expose the model to modest orientation variation during training without changing the validation inputs. The batch size is 2. These choices belong to the tutorial’s particular setup; augmentation strength and batch size should be adjusted to the task, available memory, and the transformations that make sense for the scans.
Build and compile the 3D CNN
The model stacks 3D convolutions with 3D max-pooling and batch normalization, then reduces the spatial feature maps before classification. The tutorial’s architecture and training setup are:
- Feature extraction: successive Conv3D, MaxPool3D, and batch-normalization layers learn and downsample volumetric features.
- Classification head: GlobalAveragePooling3D feeds a 512-unit dense layer, followed by dropout with a rate of 0.3 and a one-unit sigmoid output.
- Training objective: compile with binary cross-entropy and the Adam optimizer for the two-class prediction.
- Training controls: use checkpointing to retain a selected model state and early stopping to stop training when the monitored validation behavior no longer improves.
The final sigmoid value represents the model’s output for the positive class under its training labels. It should not be interpreted as a calibrated clinical probability unless calibration and clinical validity have been separately established.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret the tutorial’s results cautiously
The Keras example reports 83% accuracy when using the full dataset of more than 1,000 CT scans, alongside 6–7% variability in classification performance. These are figures reported by that tutorial, not an independent benchmark. For the smaller 200-scan subset, Keras warns that results can vary substantially: “It is important to note that the number of samples is very small (only 200) and we don’t specify a random seed. As such, you can expect significant variance in the results.” Epoch-by-epoch results from one run therefore should not be treated as a reproducible expected score.
For an experiment intended to support stronger conclusions, evaluate on data kept separate from model development and assess whether the split reflects the intended use. The demonstration does not report external validation or establish that its results transfer across hospitals, scanners, or patient populations.
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- Tensor layout: keep the channel axis and configured Keras data format consistent throughout loading, batching, and model construction.
- Preprocessing: verify that intensity handling, orientation, interpolation, and target volume dimensions suit your scans and labels.
- Split integrity: choose and record a reproducible split strategy appropriate to the data; a class-balanced split alone does not establish generalization.
- Compute: 3D tensors and convolutional feature maps can consume substantial memory. Volume dimensions and batch size are practical trade-offs to test on your hardware.
- Evaluation: report the data split and variability, and avoid presenting a demonstration score as evidence of clinical performance.
Keras maintains other code examples, but the CT tutorial is a compact implementation rather than a head-to-head comparison of 3D and 2D models. When considering another approach, compare whether it preserves cross-slice context, its memory and compute requirements, the usable input resolution, and the quantity and diversity of labeled data available.
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