A Comparison for Patch-level Classification of Deep Learning Methods on Transparent Environmental Microorganism Images: from Convolutional Neural Networks to Visual Transformers
This is an incremental comparison study for researchers in computer vision analyzing environmental microorganism images.
The paper tackled the problem of classifying Transparent Environmental Microorganism (T-EM) images by comparing deep learning methods, finding that ViT performed worst on 8x8 pixel patches but outperformed most CNNs on 224x224 pixel patches.
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.