Application of Swin Transformer for Alzheimer’s Disease Classification on Structural MRI and FDG-PET Brain Scans Using ADNI Data

Motivation: The detection of Alzheimer’s Disease (AD) can be supported by using automated computer vision solutions. Those can potentially enable an earlier diagnosis and facilitate improved patient treatment. Goal(s): Our goal is to apply a state-of-the-art deep learning approach to the field of AD diagnosis based on brain scans. Approach: A pretrained Swin Transformer model is tuned on FDG-PET and structural MRI brain scans to classify AD. Results: Our model achieves a competitive area under curve of 97.8% / 99.7% and accuracy of 97.0% / 99.5% (MRI / PET) on independent test data. Impact: We show how a modern deep neural network can be trained with reasonable efforts while still achieving comparable results to established approaches. This procedure can lead the way towards classifying AD on more challenging modalities, such as ASL.