We work with an image database of brain cells and aim to develop an unsupervised neural network, consisting of autoencoder and clustering layers, to classify whether the input image contains a cell or not. The cell types which we have tested the algorithm on, are microglia and inhibitory neurons. We feed image patches to an autoencoder with a small latent space and perform clustering on the compressed data representation from the latent space. We expect that the encodings of „positive“ and „negative“ samples would differ from each other and thus form clusters in space. The achieved results show a good seperation of the two data classes, with an F1 score reaching 0.997.