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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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Neural Network Implementation without Multiplication
Slouka, Lukáš ; Baskar, Murali Karthick (referee) ; Szőke, Igor (advisor)
The subject of this thesis is neural network acceleration with the goal of reducing the number of floating point multiplications. The theoretical part of the thesis surveys current trends and methods used in the field of neural network acceleration. However, the focus is on the binarization techniques which allow replacing multiplications with logical operators. The theoretical base is put into practice in two ways. First is the GPU implementation of crucial binary operators in the Tensorflow framework with a performance benchmark. Second is an application of these operators in simple image classifier. Results are certainly encouraging. Implemented operators achieve speed-up by a factor of 2.5 when compared to highly optimized cuBLAS operators. The last chapter compares accuracies achieved by binarized models and their full-precision counterparts on various architectures.
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Detection of significant events in systems baased on phase OTDR
Makówka, David ; Petyovský, Petr (referee) ; Valach, Soběslav (advisor)
This diploma thesis concerns the design, implementation and testing of a system that classifies events captured using optic fiber along a perimeter of guarded objects. A theoretical part introduces physical principles, main structures of measuring systems, methods of measuring, data format, pre-processing options and classification using convolutional neural networks. A practical part describes implementation of a software for convolutional neural networks training and testing, process of samples extraction from measured data, its annotation and conversion to format required by neural networks. Results of measured data analysis and results of achieved classification accuracy using convolutional neural networks for both post processing of measured data and for deployment of neural network into real time processing system are presented.
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Detection of significant events in systems baased on phase OTDR
Makówka, David ; Petyovský, Petr (referee) ; Valach, Soběslav (advisor)
This diploma thesis concerns the design, implementation and testing of a system that classifies events captured using optic fiber along a perimeter of guarded objects. A theoretical part introduces physical principles, main structures of measuring systems, methods of measuring, data format, pre-processing options and classification using convolutional neural networks. A practical part describes implementation of a software for convolutional neural networks training and testing, process of samples extraction from measured data, its annotation and conversion to format required by neural networks. Results of measured data analysis and results of achieved classification accuracy using convolutional neural networks for both post processing of measured data and for deployment of neural network into real time processing system are presented.
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Atrial Fibrillation Classification Using Deep Convolution Networks
Novotna, Petra
We propose the usage of three deep convolutional neural networks architectures for classification of a single lead electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AFIB) classification, for which data set was provided by the Department of Biomedical Engineering, BUT. The compared networks are based on ResNet, VGG net and AlexNet. Single lead signals are transformed into the form of spectrogram. AFIB data was augmented for the purpose of similar size of both respected classes and for successful classification. The most successful architecture, based on AlexNet, was found to perform obtaining an accuracy of 92 % and F1 score of 56 % on the hidden testing set.
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Convolutional Neural Networks For Identification Of Axial 2d Slices In Ct Data
Vavřinová, Pavlína
This thesis deals with the classification of 2D axial slices in CT patient’s data. The classification is realized into six categories. The sphere of convolutional neural networks was used for this purpose and AlexNet network was specifically selected for the intention of this identification, which was applied to the created data set after being adaptated. The overall classification success rate was 84%. In addition, an analysis of mistakes in classification was performed.
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Neural Network Implementation without Multiplication
Slouka, Lukáš ; Baskar, Murali Karthick (referee) ; Szőke, Igor (advisor)
The subject of this thesis is neural network acceleration with the goal of reducing the number of floating point multiplications. The theoretical part of the thesis surveys current trends and methods used in the field of neural network acceleration. However, the focus is on the binarization techniques which allow replacing multiplications with logical operators. The theoretical base is put into practice in two ways. First is the GPU implementation of crucial binary operators in the Tensorflow framework with a performance benchmark. Second is an application of these operators in simple image classifier. Results are certainly encouraging. Implemented operators achieve speed-up by a factor of 2.5 when compared to highly optimized cuBLAS operators. The last chapter compares accuracies achieved by binarized models and their full-precision counterparts on various architectures.
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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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