Deep learning on non-image medical data

Jan Pavlin (2018) Deep learning on non-image medical data. EngD thesis.

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Abstract

Use of deep learning is increasing in the last decade. This is mostly because advancement of technology and also price reduction of various graphical processors, which allow quick learning. Deep learning is an area of machine learning and has been used for computer vision, speech recognition, image classification and other. Usage of deep learning is being slowly used in medicine. In the diploma thesis, I will examine the areas of deep learning, management of insufficient data and management of unbalanced data. I will build models of different topologies of deep neural networks and compare the results achieved on the data set of medical data among them. I will also analyse the learning and use of a graphics card and a processor for learning and using neural networks. The goal of the thesis is to test and analyse the use of deep learning on medical data using different approaches to solving the problem of poor data. It is necessary to specify which topology and which methods of data preprocessing are best managed, what results they achieve and how much time is needed to learn this neural network.

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