In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation -U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists.

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This paper studies the possibility of using neural networks to classify plumage images in order to identify bird species. Taxonomic identification of bird plumage is widely used in aviation ornithology to analyze collisions with aircraft and develop methods for their prevention. This article provides a method for bird species identification based on a dataset made up in the previous research. A method for identifying birds from real-world images based on YoloV4 neural networks and DenseNet models is proposed. We present results of the feather classification task. We selected several deep learning architectures (DenseNet based) for a comparison of categorical crossentropy values on the provided dataset. The experimental evaluation has shown that the proposed method allows determining the bird species from a photo of an individual feather with an accuracy of up to 81.03 % for accurate classification, and with an accuracy of 97.09 % for the first five predictions.