  Several neurodegenerative diseases involve the accumulation of cellular DNA
damage. Comet assays are a popular way of estimating the extent of DNA damage.
Current literature on the use of deep learning to quantify DNA damage presents
an empirical approach to hyper-parameter optimization and does not include
uncertainty estimates. Deep ensemble averaging is a standard approach to
estimating uncertainty but it requires several iterations of network training,
which makes it time-consuming. Here we present an approach to quantify the
extent of DNA damage that combines deep learning with a rigorous and
comprehensive method to optimize the hyper-parameters with the help of
statistical tests. We also use an architecture that allows for a faster
computation of deep ensemble averaging and performs statistical tests
applicable to networks using transfer learning. We applied our approach to a
comet assay dataset with more than 1300 images and achieved an $R^2$ of 0.84,
where the output included the confidence interval for each prediction. The
proposed architecture is an improvement over the current approaches since it
speeds up the uncertainty estimation by 30X while being statistically more
rigorous.
