  Nowadays, analysis of Transparent Environmental Microorganism Images (T-EM
images) in the field of computer vision has gradually become a new and
interesting spot. This paper compares different deep learning classification
performance for the problem that T-EM images are challenging to analyze. We
crop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the same
proportion and then divide the two different pixel patches into foreground and
background according to ground truth. We also use four convolutional neural
networks and a novel ViT network model to compare the foreground and background
classification experiments. We conclude that ViT performs the worst in
classifying 8 * 8 pixel patches, but it outperforms most convolutional neural
networks in classifying 224 * 224 pixel patches.
