Performance contribution of data pre-processing, augmentation, and training.

To assess contributions, we took the trained ResNet-50 model (Fig 5) as a baseline method and assessed the impact of removing components of our methods pipeline. Each computational experiment was replicated five-fold (five separate training runs for each model), allowing statistical comparison of approaches. Across replicates we report average (+/- standard deviation) accuracy (the number of correct predictions divided by the total number of predictions x100) and loss (a summation of the errors made for each sample). Statistical comparisons are summarized in S2 Table.