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Microglia are the macrophages resident in the central nervous system. Brain injuries, such as traumatic brain injury, hypoxia, and stroke, can induce inflammatory responses accompanying microglial activation. The morphology of microglia is notably diverse and a prominent manifestation of activation. In this study, we propose to classify activated microglia using a convolutional neural network (CNN). Iba1 images were acquired from a control and cardiac arrest Long-Evans rat brain with a bright-field microscopy. The training data of 54,333 single-cell images were collected from the cortex and midbrain areas and curated by experienced neuroscientists. Results were compared between CNNs with different architectures, including Resnet18, Resnet50, Resnet101, and support vector machine classifiers. The highest model performance was found by Resnet18, trained after 120 epochs with a classification accuracy of 95.5-98.8 percent. The findings indicate a potential application for using CNN in the quantitative analysis of microglial morphology over regional differences in a large brain section.