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Presenting the approach to building an automated system for grading the microscopy scans of biopsied tissue. The system consists of convolutional neural network (CNN) and a recurrent neural network (RNN) chained together to predict the ISUP grade group of a tissue sample. High resolution microscopy scan is split into a grid of smaller square tiles. Each tile containing the tissue is mapped into a feature vector by applying the CNN (DenseNet121). Then feature vectors (presented as a sequence) are passed to RNN (GRU units) to evaluate the presence of cancerous tissue and to assign a corresponding grade group.