Deep Learning Based Neural Network for Six-Class-Classification of Alzheimer's Disease Stages Based on MRI Images
- State of the art classifiers split Alzheimer’s disease progression into a limited number of stages and use a comparatively small database. For the best treatment, it is desirable to have the highest resolution from the progression of the disease. This paper proposes a reliable deep convolutional neural network for the classification of six different Alzheimer’s disease stages based on Magnetic Resonance Imaging (MRI). The peculiarity of this paper is the introduction of a new, sixth, disease stage, and the large amount of data that has been taken into account. Additionally, not only the testing accuracy is analyzed, but also the robustness of the classifier to have feedback on how certain the neural network makes its predictions.
| Verfasserangaben: | Tim RörupORCiD, Peter GlösekötterORCiD, Hector PomaresORCiD, Ignacio RuizORCiD |
|---|---|
| DOI: | https://doi.org/10.1007/978-3-030-85030-2_1 |
| Titel des übergeordneten Werkes (Deutsch): | IWANN2021 |
| Verlag: | Springer International Publishing |
| Dokumenttyp: | Beitrag in einer Konferenzveröffentlichung |
| Sprache: | Deutsch |
| Datum der Veröffentlichung (online): | 30.09.2021 |
| Jahr der Erstveröffentlichung: | 2021 |
| Veröffentlichende Institution: | FH Münster - University of Applied Sciences |
| Datum der Freischaltung: | 30.09.2021 |
| Freies Schlagwort / Tag: | Deep Learning |
| Erste Seite: | 3 |
| Letzte Seite: | 14 |
| Fachbereiche: | Elektrotechnik und Informatik (ETI) |
| Publikationsliste: | Glösekötter, Peter |
| Lizenz (Deutsch): |  Bibliographische Daten |
