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The advancement of the neuroscientific imaging techniques has produced anunprecedented size of neural cell imaging data, which calls for automatedprocessing. In particular, identification of cells from two photon imagesdemands segmentation of neural cells out of various materials andclassification of the segmented cells according to their cell types. Toautomatically segment neural cells, we used U-Net model, followed byclassification of excitatory and inhibitory neurons and glia cells using atransfer learning technique. For transfer learning, we tested three publicmodels of resnet18, resnet50 and inceptionv3, after replacing the fullyconnected layer with that for three classes. The best classificationperformance was found for the model with inceptionv3. The proposed applicationof deep learning technique is expected to provide a critical way to cellidentification in the era of big neuroscience data.

Automated identification of neural cells in the multi-photon images using deep-neural networks