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Many scientific works rely on analysis of images of microbiological objects. it can be object count, object classification, shape and color analysis etc. Such analysis al- ways is time consuming and there is also a window for errors. Threrefore this process was from its beggining a target for numerous attempts of automation. Most successfull tries are based on different segmentation methods. All mentioned approaches are using deterministic calculations. Aim of this work is to bring another kind of image segmentation to this task - neural networks. To successfully implement to image segmentation a set of examples is required. Size of such set should be at least no less than several hundreds of images. So for this purpose photographs of irradiated yeast were chosen mainly because it is easy to segment them manually with enough accuracy. A secondary task was to make an attempt to distinguish life cell from the dead ones together with segmentation. All calculation are done using Google Colaboratory as a computational background. [ABSTRACT FROM AUTHOR]

An electrocardiogram is a simple test that can be used to check heart's rhythm and electrical activity and diagnose several abnormal arrhythmias as well. Most of studies try to categorize some sequence of beats and in most successful models the key feature for classification is RR-interval. Our research aims to check whether it is possible to successfully classify ECG heartbeats using scalograms and machine learning algorithms, convolutional neural networks, in particular. All records of necessary signals were taken from open-source PhysioBank Databases from research resource for complex physiologic signals known as PhysioNet. ECG recordings were parsed into sequences of single beats. Due to preprocessing and described model architecture a 92% accuracy has been achieved. Proposed model is still lacking some performance in comparison with state-of-the-art solutions in ECG heart categorization. However, it is possible to modify applied approach. [ABSTRACT FROM AUTHOR]