Analysis of dendritic morphology and mRNA spatial distributions using machine learning approaches. A) A schematic representation of the CNN model utilizing binary masks, Z-stacks, and maximum projection images of dendrites, integrated with one-hot encoded gene identity vectors, to predict cluster labels associated with mRNA distribution. B) Confusion matrices for the CNN classifier showing the performance on training and test datasets, highlighting the challenge of achieving high accuracy due to the unbalanced nature of the data. C) GradCAM visualizations demonstrating the convolutional layer activations associated with the various predicted clusters, which suggest patterns but do not conclusively identify specific morphological features across layers.