Artificial neural networks have been gaining attention in recent years due to theirimpressive ability to map out complex nonlinear relations within data. In this report,an attempt is made to use a Long short-term memory neural network for detectinganomalies within electrocardiographic records. The hypothesis is that if a neuralnetwork is trained on records of normal ECGs to predict future ECG sequences, it isexpected to have trouble predicting abnormalities not previously seen in the trainingdata. Three different LSTM model configurations were trained using records fromthe MIT-BIH Arrhythmia database. Afterwards the models were evaluated for theirability to predict previously unseen normal and anomalous sections. This was doneby measuring the mean squared error of each prediction and the uncertainty of over-lapping predictions. The preliminary results of this study demonstrate that recurrentneural networks with the use of LSTM units are capable of detecting anomalies.

Diploma thesis is aimed to trainable image segmentation using deep neural networks. In the paper is explained the principle of digital image processing and image segmentation. In the paper is also explained the principle of artificial neural network, model of artificial neuron, training and activation of artificial neural network. In practical part of the paper is created an algorithm of sliding window to generate sub-images from image from magnetic rezonance. Generated sub-images are used to train, test and validate of the model of neural network. In practical part of the paper si created the model of the artificial neural network, which is used to trainable image segmentation. Model of the neural network is created using the Deeplearning4j library and it is optimized to parallel training using Spark library.