The work is devoted to the use of artificial neural networks to solve the problem of recognition and isolation of objects (cells) in digital micrographs used in the practice of microbiological research. This task is relevant due to a combination of two factors: a large amount of data (hundreds and thousands of photographs, hundreds of objects on each) arising from such studies, as well as the high complexity of manual image processing and the risk of operator errors. To solve the problem of recognition and separation of objects (cells) in digital biomedical micrographs, artificial convolutional neural networks are used. However, in most cases, the use of artificial neural networks requires selection of the network architecture and its training for each task individually. Each time when solving the problem of training the network, it is necessary to collect huge training samples, the more complex the more complex the architecture. The task of cell isolation was solved using a convolutional neural network. The neural network architecture of Mask R-CNN Facebook Research was used. We used a pretrained neural network, retrained in digital micrographs obtained and marked out by the authors. The solution is implemented in Python using TensorFlow, an open source machine learning software library developed by Google. As a result of testing the system, on the available data, the correctness of cell recognition in microphotographs was more than 95%, despite the small size of the digital images used. The applied approach definitely showed its efficiency on the available experimental data and has development prospects in the direction of increasing the size of processed images, increasing recognition accuracy, expanding the composition of secreted objects, working not only with cells, but also with structures in tissues.
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