  High hospitalization rates due to the global spread of Covid-19 bring about a
need for improvements to classical triaging workflows. To this end,
convolutional neural networks (CNNs) can effectively differentiate critical
from non-critical images so that critical cases may be addressed quickly, so
long as there exists some representative image for the illness. Presented is a
conglomerate neural network system consisting of multiple VGG16 CNNs; the
system trains on weighted skin disease images re-labelled as critical or
non-critical, to then attach to input images a critical index between 0 and 10.
A critical index offers a more comprehensive rating system compared to binary
critical/non-critical labels. Results for batches of input images run through
the trained network are promising. A batch is shown being re-ordered by the
proposed architecture from most critical to least critical roughly accurately.
