biostudies-literatureStern UNIGMS NIH HHS14351https://www.ebi.ac.uk/biostudies/studies/S-EPMC4585819biostudies-literatureUnknown5High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals' body parts. But the image analysis rarely attempts to recognize "behavioral states"-e.g., actions or facial expressions-directly from the image instead of using the detected body parts. Here, we show that convolutional neural networks (CNNs)-a machine learning approach that recently became the leading technique for object recognition, human pose estimation, and human action recognition-were able to recognize directly from images whether Drosophila were "on" (standing or walking) or "off" (not in physical contact with) egg-laying substrates for each frame of our videos. We used multiple nets and image transformations to optimize accuracy for ourScientific reportsAnalyzing animal behavior via classifying each video frame using convolutional neural networks.PMC4585819R01GM100027R01 GM100027Stern UYang CHHe RfalseAnalyzing animal behavior via classifying each video frame using convolutional neural networks.High-throughput analysis of animal behavior requires software to analyze videos. Such software analyzes each frame individually, detecting animals' body parts. But the image analysis rarely attempts to recognize "behavioral states"-e.g., actions or facial expressions-directly from the image instead of using the detected body parts. Here, we show that convolutional neural networks (CNNs)-a machine learning approach that recently became the leading technique for object recognition, human pose estimation, and human action recognition-were able to recognize directly from images whether Drosophila were "on" (standing or walking) or "off" (not in physical contact with) egg-laying substrates for each frame of our videos. We used multiple nets and image transformations to optimize accuracy for our2015-01-01T00:00:00Z2015 Sep2025-04-21T20:21:17.844Z2019-03-27T01:59:06ZS-EPMC45858192639469510.1038/srep14351