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Detection of malignant melanoma in histological sample using deep neural networks
Frey, Adam ; Lokoč, Jakub (advisor) ; Straka, Milan (referee)
The aim of this thesis is to create a classification method for detection of ma- lignant melanoma in high-resolution digital images. Deep convolutional neural networks were used for this task. At first, a short overview of malignant melanoma and ways to detect it is presented. Deep convolutional neural networks are also introduced with a special attention given to models used further in this work. Several ways to generate samples from the provided histological images are discussed, and several experiments are evaluated to decide how to maximize the accuracy of employed classification methods. The thesis then focuses on several neural network structures used for image classification and their possible utiliza- tion for the given task. The emphasis is laid on the transfer learning, a method used for modifying already trained models for different tasks. This method is then used for training several classifiers. Further on, several methods for the visualization of model results are discussed with some of them implemented. The experiments show promising results on par with other studies dealing with similar problems. Several possibilities for further development are listed in the conclusion.
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Gaussian filters with rotating kernel
Vintr, Tomáš ; Martišek, Dalibor (referee) ; Druckmüller, Miloslav (advisor)
The objective of this thesis is to create Gaussian 1D filters with rotating kernel theory which enables to program algorithm for noise reduction and beam structure highlighting in a digital picture of the solar corona. A fragment of original picture of solar corona and of pictures filtred by this algorithm is in the enclosure.
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