The aim of the thesis is the classification of magnetic resonance images by Deep Learning models. The goal was to predict Alzheimer's disease on the dataset created by Alzheimer's Disease Neuroimaging Initiative (ADNI). To prepare the dataset, we built two processing pipelines, which align, normalise and remove irrelevant features from brain scans. We used the processed scans for a 2D and 3D dataset. We designed a few models based on convolutional and previously proposed architectures. Although, many studies published astonishing results on ADNI classification, the results of our experiments do not support previous research in this area. Contrary to what was previously thought, we found that the accuracy strongly depends on the dataset splitting. If we split the dataset by patients, not by scans, the accuracy drops significantly. We presented an overview of several previously published architectures and our experiments showing results of these architectures on the datasets generated by random splitting or subject-based splitting. We also pointed out how the dataset splitting choice changes the performance of our models. The work is a natural extension of study [Fung et al., 2019]. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:434928 |
| Date | January 2020 |
| Creators | Karella, Tomáš |
| Contributors | Pilát, Martin, Blažek, Jan |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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