Manual quantification of activated cells can provide valuable information about stimuli-induced changes within brain regions; however, this analysis remains time intensive. Therefore, we created SimpylCellCounter (SCC), an automated method to quantify cells that express cFos protein, an index of neuronal activity, in brain tissue and benchmarked it against two widely-used methods: OpenColonyFormingUnit (OCFU) and ImageJ Edge Detection Macro (IMJM). In Experiment 1, manually-obtained cell counts were compared to those detected via OCFU, IMJM and SCC. The absolute error in counts (manual versus automated method) was calculated and error types were categorized as false positives or negatives. In Experiment 2, performance analytics of OCFU, IMJM and SCC were compared. In Experiment 3, SCC analysis was conducted on images it was not trained on, to assess its general utility. We found SCC to be highly accurate and efficient in quantifying cells with circular morphologies that expressed cFos. Additionally, SCC utilized a new approach to count overlapping cells with a pretrained convolutional neural network classifier. The current study demonstrates that SCC is a novel, automated tool to quantify cells in brain tissue and complements current, open-sourced methods designed to detect cells in vitro.