  Galaxy clusters identified from the Sunyaev Zel'dovich (SZ) effect are a key
ingredient in multi-wavelength cluster-based cosmology. We present a comparison
between two methods of cluster identification: the standard Matched Filter (MF)
method in SZ cluster finding and a method using Convolutional Neural Networks
(CNN). We further implement and show results for a `combined' identifier. We
apply the methods to simulated millimeter maps for several observing
frequencies for an SPT-3G-like survey. There are some key differences between
the methods. The MF method requires image pre-processing to remove point
sources and a model for the noise, while the CNN method requires very little
pre-processing of images. Additionally, the CNN requires tuning of
hyperparameters in the model and takes as input, cutout images of the sky.
Specifically, we use the CNN to classify whether or not an 8 arcmin $\times$ 8
arcmin cutout of the sky contains a cluster. We compare differences in purity
and completeness. The MF signal-to-noise ratio depends on both mass and
redshift. Our CNN, trained for a given mass threshold, captures a different set
of clusters than the MF, some of which have SNR below the MF detection
threshold. However, the CNN tends to mis-classify cutouts whose clusters are
located near the edge of the cutout, which can be mitigated with staggered
cutouts. We leverage the complementarity of the two methods, combining the
scores from each method for identification. The purity and completeness of the
MF alone are both 0.61, assuming a standard detection threshold. The purity and
completeness of the CNN alone are 0.59 and 0.61. The combined classification
method yields 0.60 and 0.77, a significant increase for completeness with a
modest decrease in purity. We advocate for combined methods that increase the
confidence of many lower signal-to-noise clusters.
