  The class distribution of data is one of the factors that regulates the
performance of machine learning models. However, investigations on the impact
of different distributions available in the literature are very few, sometimes
absent for domain-specific tasks. In this paper, we analyze the impact of
natural and balanced distributions of the training set in deep learning (DL)
models applied on histological images, also known as whole slide images (WSIs).
WSIs are considered as the gold standard for cancer diagnosis. In recent years,
researchers have turned their attention to DL models to automate and accelerate
the diagnosis process. In the training of such DL models, filtering out the
non-regions-of-interest from the WSIs and adopting an artificial distribution
(usually, a balanced distribution) is a common trend. In our analysis, we show
that keeping the WSIs data in their usual distribution (which we call natural
distribution) for DL training produces fewer false positives (FPs) with
comparable false negatives (FNs) than the artificially-obtained balanced
distribution. We conduct an empirical comparative study with 10 random folds
for each distribution, comparing the resulting average performance levels in
terms of five different evaluation metrics. Experimental results show the
effectiveness of the natural distribution over the balanced one across all the
evaluation metrics.
