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Among all types of cancer, breast cancer is the most common. In 2017 breast cancer was the fourth rate for death reasons in Ukraine. The paper is devoted to the automatization of histopathological analysis, which can improve the process of cancer stage diagnosis. The purpose of the paper is to research the ability to use convolutional neural networks for classifying biopsy images for cancer diagnosis. The tasks of research are: analyzing cancer statistics in Europe and Ukraine; analyzing usage of Machine Learning in cancer prognosis and diagnosis tasks; preprocessing of BreCaHAD dataset images; developing a convolutional neural network and analyzing results; the building of heatmap. The object of the research is the process of detecting tumors in microscopic biopsy images using Convolutional Neural Network. The subject of the research is the process of classifying healthy and cancerous cells using deep learning neural networks. The scientific novelty of the research is using ConvNet trained on the BreCaHAD dataset for histopathological analysis. The theory of deep learning neural networks and mathematical statistics methods are used. In result it is obtained that the classification accuracy for a convolutional neural network on the test data is 0.935, ConvNet was effectively used for heatmap building. K e ywor d s : deep learning; convolutional neural networks; breast cancer; histopathological analysis; biopsy images; BreCaHAD.

Neural networks are intensively developed and used in all spheres of human activity in the modern world. Their use to determine the fire hazardous forest areas can begin to solve the problem of preventing wildfires. In recent years, wildfires have acquired enormous proportions. Wildfires are difficult to control and, if they occur, require a large amount of resources to eliminate them. The paper is devoted to solve the problem of identifying fire hazardous forest areas. The Camp Fire (California, USA) areas are considered. The purpose of the paper is to research the possibility of using convolutional neural networks for the detection fire hazardous forest areas using multispectral images obtained from Landsat 8. The tasks of research are finding the territories where the largest fires occurred in recent time; analyzing economic and ecologic losses from wildfires; receiving and processing multispectral images of wildfire areas from satellite Landsat 8; calculation of spectral indices (NDVI, NDWI, PSRI); developing convolutional neural network and analyzing results. The object of the research is the process of detecting fire hazardous forest areas using convolutional neural network. The subject of the research is the process of recognition multispectral images using deep learning neural network. The scientific novelty of the research is the recognition method of multispectral images by using convolutional neural network has been improved. The theory of deep learning neural networks, the theory of recognition multispectral images and mathematical statistics methods are used. The spectral indices for allocating the object under research (green vegetation, humidity, dry carbon) were calculated. It is obtained that the classification accuracy for a convolutional neural network on the test data is 94.27%. K e ywor d s : deep learning; convolutional neural networks; multispectral images; spectral indices; fire hazardous forest areas.

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This chapter uses deep learning neural networks for processing of aerospace system multispectral images. Convolutional and Capsule Neural Network were used for processing multispectral images from satellite Landsat 8, previously processed using spectral indices NDVI, NDWI, PSRI. The authors' approach was applied to wildfire Camp Fire (California, USA). The deep learning neural networks are used to solve the problem of detecting fire hazardous forest areas. Comparison of Convolutional and Capsule Neural Network results was done. The theory of neural networks of deep learning, the theory of recognition of multispectral images, methods of mathematical statistics were used.