Early Diagnosis of Alzheimer through Swin-Transformer-Based Deep Learning Framework using Sparse Diffusion Measures

Alzheimer disease is one of the most common neuro-degenerative diseases, with an estimated 6.2 million cases in the United States. This research article investigates the potential of Transformer-based deep learning techniques to accelerate the processing of diffusion tensor imaging (DTI) measures and improve the early diagnosis of Alzheimer disease (AD) using sparse data. Diffusion Weighted Imaging (DWI) is a time-consuming process, with each diffusion direction taking between 2-5 minutes, and at least 40 diffusion directions are needed for routine clinical diagnosis, which needs scanning duration exceeding 3 hours for each patient. By leveraging the attention mechanism, our proposed model generates quantitative measures of fractional anisotropy (FA), axial diffusivity (AxD), and mean diffusivity (MD) using 5 and 21 diffusion directions, making it useful for clinical diagnosis through reduced scanning time of more than half. Our experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed model outperforms the traditional linear least square method, achieving accurate quantitative measurement of FA, AxD, and MD scores for early diagnosis of AD patients from healthy controls using sparse diffusion directions. Our analysis highlights the potential of Swin-Transformer attention-based deep learning framework to improve the early diagnosis and treatment of Alzheimer's disease. A repositories for our research work at https://github.com/ AbhishekTiwari101/ACML2023-Early-Diagnosis-of-Alzheimer-via-Deep-Learning https://github.com/reachananya/Early-diagnosis-of-Alzheimer-via-DL Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:ADNI Acknowledgement List