4D Convolutional Neural Network in Matlab

How to implement a CNN in MATLAB to train a 4D image with the 4th dimension beeing frequency??

Answer 1

[Parag]

Hi, in MATLAB, when dealing with 4D images where the fourth dimension represents frequency, there are two key implementation approaches depending on how the frequency information is handled:

1.Treating Frequency as Additional Channels - Reshape the dataset to merge the frequency dimension into the channel dimension: Xtrain:(H,W,C,F,N)→(H,W,C×F,N) . The CNN treats different frequency bands as additional feature channels, similar to how colour channels (RGB) work.

2.Treating Frequency as an Additional Input Dimension - Instead of using “imageInputLayer”, use custom 3D convolutions (convolution3dLayer) to process the frequency dimension separately: Xtrain:(H,W,C,F,N)

While the first approach has simple implementation leveraging built in MATLAB deep learning tools it loses explicit frequency structure.

MATLAB Implementation of a 3D CNN for 4D Images on random data

% Define Dummy Data 
imageSize = [32, 32, 3, 10];  % (Height, Width, Channels, Frequency) 
numSamples = 200;  % More samples improve learning 
numClasses = 5;  % Classification task 

% Generate Random 4D Data (height, width, channels, frequency, samples) 
XTrain = rand(imageSize(1), imageSize(2), imageSize(3), imageSize(4), numSamples); 
YTrain = categorical(randi(numClasses, [numSamples, 1])); % Random labels 
% Define a deeper 3D CNN 
layers = [ 
    image3dInputLayer(imageSize, 'Normalization', 'none') 
    % First convolutional block 
    convolution3dLayer([3 3 3], 32, 'Padding', 'same') 
    batchNormalizationLayer 
    reluLayer 
    maxPooling3dLayer([2 2 1], 'Stride', [2 2 1])   
     
    % Second convolutional block 
    convolution3dLayer([3 3 3], 64, 'Padding', 'same') 
    batchNormalizationLayer 
    reluLayer 
    maxPooling3dLayer([2 2 1], 'Stride', [2 2 1]) 

    % Third convolutional block 
    convolution3dLayer([3 3 3], 128, 'Padding', 'same') 
    batchNormalizationLayer 
    reluLayer 
    % Fourth convolutional block 
    convolution3dLayer([3 3 3], 256, 'Padding', 'same') 
    batchNormalizationLayer 
    reluLayer 
    maxPooling3dLayer([2 2 2], 'Stride', [2 2 2])   
   
    % Fully connected layers 
    fullyConnectedLayer(512) 
    reluLayer 
    dropoutLayer(0.5)  % Reduces overfitting 
    fullyConnectedLayer(numClasses) 
    softmaxLayer 
    classificationLayer]; 


% Training Options with Adam Optimizer 
options = trainingOptions('adam', ... % Faster convergence than SGDM 
    'InitialLearnRate', 0.001, ... 
    'MaxEpochs', 20, ... % More epochs for better learning 
    'MiniBatchSize', 16, ... 
    'Verbose', true, ... % Ensures loss printing 
    'Plots', 'training-progress'); % Visualize training 

% Train the Network 
net = trainNetwork(XTrain, YTrain, layers, options);

You can also refer this paper for information about 4-d convlutions

https://arxiv.org/pdf/2002.07442

Please refer this MATLAB documentation for “convn” function in MATLAB

https://www.mathworks.com/help/matlab/ref/convn.html