Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis
Fig 2
Proposed Fused CNN architecture.
The network comprises of two branches: Imaging feature extractor (1, green-top) and Non-imaging feature extractor (2, red-bottom). The output of (1) is a 512x[3x3x3] array, which was subsequently average pooled into a 512x1 vector. This vector is compressed into a 24x1 vector using a fully-connected layer before being concatenated with the 7x1 vector outputted from branch (2). The 31x1 concatenated vector is fed into a Classifier (3, violet-bottom), which performs the classification tasks. During the training, classification was performed on both branch-specific latent vectors (forming and
) and from the Classifier itself (forming
). However, in validation, only prediction from the Classifer was taken into account.