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You can build a 3D convolutional neural network (3D CNN) in Keras to classify CT volumes by adapting the official Keras example. It loads NIfTI scans, clips and normalizes voxel intensities, resizes each volume, and feeds batches to Conv3D layers. The example predicts the dataset’s “normal” or “abnormal” label; it is an educational implementation, not a diagnostic tool or evidence of clinical performance.
What the Keras example does
A 2D CNN analyzes one image at a time. A 3D CNN applies convolution along three spatial dimensions, letting the model use spatial context across the volume rather than treating each CT slice as an unrelated image. Keras describes Conv3D as a 3D convolution over volumes and uses a five-dimensional tensor for batched input: batch, spatial dimensions, and channels. See the Keras Conv3D API.
The Keras CT classification example by Hasib Zunair uses a subset of MosMedData. Its binary labels are the example dataset’s normal and abnormal groups, associated with radiological findings and described in the tutorial in relation to viral pneumonia. They are dataset labels, not a validated diagnosis of an individual patient.
Prepare CT volumes for the model
Load NIfTI data and scale intensities
The example uses Nibabel to load CT scans stored in NIfTI format and retrieve their voxel values. It treats CT intensity as Hounsfield units (HU), clips values below −1000 HU and above 400 HU, then scales the clipped range to floating-point values between 0 and 1. Clipping limits the range presented to the model; normalization puts values on a consistent numerical scale.
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Rotate and resize each volume
After preprocessing, the tutorial rotates and resizes scans to a spatial shape of 128 × 128 × 64 voxels, using interpolation during resizing. These are choices for this example, not a universal CT preprocessing standard. For another task, acquisition protocol, or dataset, check that the chosen intensity transform, orientation, interpolation, and target dimensions preserve the information needed for its labels.
Organize labels and split the data
The selected subset contains 200 scans: 100 in each class. The example assigns binary labels from the normal and abnormal directories and splits each class into 70 training scans and 30 validation scans. That produces 140 training scans and 60 validation scans, balanced across the two labels.
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The tutorial does not specify a random seed for this split. A different run can therefore use a different partition; record the split and seed in your own experiment if you need to reproduce it.
Shape data for Conv3D
In the example’s channels-last configuration, one preprocessed scan has shape (128, 128, 64, 1): three spatial dimensions followed by one grayscale channel. Add that final channel dimension to each volume. A batch adds the sample axis at the front, so a batch of two scans has shape (2, 128, 128, 64, 1).
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Conv3D also supports a channels-first layout. When adapting code, make sure the model’s data format and the position of the channel axis agree; otherwise, the model may reject the input or interpret a dimension incorrectly.
Build and train the 3D CNN
Augment training volumes
The example applies small random-angle rotations to training data only. Validation scans receive the channel dimension but not random rotation, so validation measures the model on the prepared volumes rather than augmented versions. Its batch size is 2.
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Define the model and objective
The network stacks Conv3D and MaxPool3D blocks with batch-normalization layers, then reduces the spatial output with GlobalAveragePooling3D. A 512-unit dense layer and dropout at 0.3 precede a single sigmoid output for the binary label. The example compiles the model with binary cross-entropy and Adam, and includes checkpointing and early stopping during training.
For a runnable implementation, including the imports, data preparation, model definition, and training code, follow the complete Keras example. It uses Keras with TensorFlow, NumPy, Nibabel, and SciPy, and links to a Colab entry from the official examples ecosystem.
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Interpret the reported results cautiously
Keras reports 83% accuracy when using the full dataset of more than 1,000 CT scans and notes 6–7% variability in classification performance. Those figures are the tutorial’s reported results, not an independent benchmark or clinical performance estimate. For the 200-scan subset, Keras warns: “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.” The example’s epoch-by-epoch results fluctuate, so a single run should not be treated as expected or reproducible performance.
The tutorial does not establish external validation, clinical utility, regulatory status, or performance across institutions. To assess a model for a real application, those questions would need evidence beyond this educational demonstration.
When a 3D CNN is a reasonable starting point
A volumetric model is useful when relationships across adjacent slices matter to the classification task. It also means processing a volume rather than isolated 2D images, so input resolution, memory and computation, and the quantity and diversity of labeled data are practical considerations. The Keras tutorial does not quantify these trade-offs against alternative architectures; treat them as evaluation questions for your own data, not as a performance ranking.
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