Перегляд за Автор "Yaloveha, Vladyslav"
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Документ Application of convnet for histopathological analysis(ФОП Петров В. В., 2019) Hlavcheva, Daria; Yaloveha, Vladyslav; Podorozhniak, AndriiДокумент Application of convolutional neural network for histopathological analysis(Національний технічний університет "Харківський політехнічний інститут", 2019) Hlavcheva, Daria; Yaloveha, Vladyslav; Podorozhniak, AndriiAmong 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 tasksof 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 noveltyof 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 resultit is obtained that the classification accuracy for a convolutional neural networkon the test data is 0.935, ConvNet waseffectively used for heatmap building.Документ A comparison of classifiers applied to the problem of biopsy images analysis(Національний технічний університет "Харківський політехнічний інститут", 2020) Hlavcheva, Daria; Yaloveha, Vladyslav; Podorozhniak, Andrii; Lukova-Chuiko, NataliiaThe purpose of the research is to compare classification algorithms for the histopathological images analyzing issue and to optimize the parameters for obtaining better classification accuracy. The following tasks are solved in the article: preprocessing of BreCaHAD dataset images, implementation and training of CNN, applying K-nearest neighbours, SVM, Random Forest,XGBoost, and perceptron algorithms for classifying features that were extracted by CNN, and results comparison. The object of the research is the process of classifying tumor cells in the microscopic biopsy images. The subject of the research is the processof using ML algorithms for classification of the features extracted by CNN from input biopsy image. The scientific novelty of the research is a comparative analysis of classifiers on the task of “tumor” and “healthy” cells images classification from processed BreCaHAD dataset. As a result it was obtained that from chosen classifiers SVM reached the highest accuracy on test data –0.972. This is the only algorithm that shows better accuracy than perceptron. Perceptron gets 0.966 classification accuracy. K-nearest neighbours, Random Forest, and XGBoost algorithms reached lower results. The algorithms' hyperparameters optimization was carried out. The results have been compared with related works. The following research methodsare used: the theory of deep learning, mathematical statistics, parameters optimization.Документ Using of convolutional neural network for histopathology analysis(ТОВ "Планета-Прінт", 2021) Hlavcheva, Daria; Yaloveha, Vladyslav; Podorozhniak, Andrii