Artificial intelligence algorithms allow computers to perform intelligent tasks. Big impact is expected in medicine where such algorithms could become a standard in the day-today work of doctors. Pathology is a branch of medicine that studies disease. A big part of it is related to microscopic disease diagnostics, which means pathologist work with images. Deep neural networks are inspired by the way the brain processes information and are particularly suitable for working with images. In this thesis, a method for detecting metastatic tissue in histopatological images of lymph node sections using deep neural networks is proposed. The proposed method achieves 86,57% accuracy on the PatchCamelyon data set and is compared with other published methods. PatchCamelyon is a collection of 327 680 color images with dimensions 96 x 96. For each image, there are annotations of metastatic tissue presence in the central image region. This data set was generated by extracting segments of 400 whole-slide images from the Camelyon16 data set. Augmentation of the PatchCamelyon data set significantly influences the accuracy of the proposed method. Using a larger training data set could increase the accuracy of this method. This method can be used on an average computer in real-time, as it can process 250 images per second. By increasing the classification threshold, it is possible to increase the accuracy of the proposed method so it can be used for fast detection of metastases in whole-slide images of lymph nodes. This method could accelerate the work of pathologists by marking regions of a whole-slide image which are not part of the normal lymph node tissue.