The problem of forming a training set for the task of image processing is considered. It is shown that this task is of great importance in the construction of intelligent medical diagnostic systems in which convolution neural networks are used for image processing (results of ultrasound, CT and MRI). Due to the lack of elements of the training sample, it is proposed, on the one hand, to use approaches of artificial data multiplication based on the initial training sample of a fixed volume, and on the other hand, to use methods that reduce the need for large training samples, both through the use of ensemble topology (hybrid neural networks), and by applying the transfer learning approach. An algorithm for the formation of a training set for image processing tasks is developed based on the modification of the initial input information with the calculation of the confidence measure of the obtained sample.
It is considered the problem of structural-parametric synthesis of a hybrid neural networks based on the use of Group Method of Data Handling neural network. Hybridization is achieved through the use of various neurons: classical, nonlinearAdaline, R-neuron, W-neuron, Wavelet-neuron. The problem of structural-parametric synthesis of hybrid neural network consists in the optimal choice of the number of layers, the number of neurons in the layers, the order of alternation of layers with different neurons. As an example it is considered the forecast problem solution with help of hybrid neural networks based on the data of the COVID-19 pandemic, collected by Johns Hopkins University. A MAPE criterion was used for quality assessment.IndexTerms-Hybrid neural network; structural-parametric synthesis; forecast of time series.Направление научной деятельности: системный анализ, искусственные нейронные сети. Количество публикаций: более 80 научных работ.
The problem of generating training data for setting up the convolutional neural networks is considered, which is of great importance in the construction of intelligent medical diagnostic systems, where due to the lack of elements of the training sample, it is proposed to use the approaches of artificial data multiplication based on the initial training sample of a fixed size for the image processing (the results of the ultrasound, CT and MRI). It shows that the increase of the training sample resulted in lessinformative and poor quality elements, which can introduce extra errors in the goal achievement. To eliminate this situation the algorithm for assessing the quality of a sample element with the subsequent removal of uninformative elements is proposed.
This paper considers the structural-parametric synthesis (SPS) of neural networks (NNs) of deep learning, in particular convolutional neural networks (CNNs), which are used in image processing. It has been shown that modern neural networks may possess a variety of topologies. That is ensured by using unique blocks that determine their essential features, namely, the compression and excitation unit, the attention module convolution unit, the channel attention module, the spatial attention module, the residual unit, the ResNeXt block. This, first of all, is due to the need to increase their efficiency in the processing of images. Due to the large architectural space of parameters, including the type of unique block, the location in the structure of the convolutional neural network, its connections with other blocks, layers, computing costs grow nonlinearly. To minimize computational costs while maintaining the specified accuracy this work set tasks of both the generation of possible topology and structural-parametric synthesis of convolutional neural networks. To resolve them, the use of a genetic algorithm (GA) has been proposed. Parameter configuration was implemented using a genetic algorithm and modern gradient methods (GM). For example, stochastic gradient descent with momentum, accelerated Nesterov gradient, adaptive gradient algorithm, distribution of the root of the mean square of the gradient, assessment of adaptive momentum, adaptive Nesterov momentum. It is assumed to use such networks in the intelligent medical diagnostic system (IMDS), for determining the activity of tuberculosis. To improve the accuracy of solving the classification problem in the processing of images, the ensemble structure of hybrid convolutional neural networks (HCNNs) has been proposed in the current work. The parallel structure of the ensemble with the merged layer was used. Algorithms of optimal choice and integration of features in the construction of the ensemble have been developed
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