Abstract
Alzheimer’s Disease (AD) imposes a heavy burden on health services both due to the large number of people affected as well as the high costs of medical care. Recent research efforts have been dedicated to the development of computational tools to support medical doctors in the early diagnosis of AD. This paper is focused into studying the capacity of Deep Learning (DL) techniques to automatically identify AD based on PET neuroimaging. PET images of the cerebral metabolism of glucose with fluorodeoxyglucose (\(^{18}\)F-FGD) were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Two DL approaches are compared: a 2D Inception V3 pre-trained model and a custom end-to-end trained 3D CNN to take advantage of the spatial patterns of the full FDG-PET volumes. The results achieved demonstrate that the PET imaging modality is suitable indeed to detect early symptoms of AD. Further to that, the carefully tuned custom 3D CNN model brings computational advantages, while keeping the same discrimination capacity as the exhaustively pre-trained 2D Inception V3 model.
This research work is funded by National Funds through the FCT - Foundation for Science and Technology, in the context of the project UIDB/00127/2020.
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Bastos, J., Silva, F., Georgieva, P. (2022). Deep Learning for Diagnosis of Alzheimer’s Disease with FDG-PET Neuroimaging. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_8
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