Early detection of Alzheimer’s disease in structural and functional MRI
The novelty of this work lies in the seamless integration of advanced segmentation techniques with hybrid classifiers, offering a robust and scalable framework for early AD detection. The proposed study demonstrates a significant advancement in the early detection of Alzheimer's disease by integrating state-of-the-art deep learning models and comprehensive functional connectivity analysis. This early detection capability is crucial for timely intervention and better management of the disease in neurodegenerative disorder diagnostics.
Authors
- Zaid Ali Alhababi
- Tahani Jaser Alahmadi (ORCID: https://orcid.org/0000-0002-0067-692X)
- U. Snekhalatha (ORCID: https://orcid.org/0000-0002-4032-1349)
- Hend Khalid Alkahtani (ORCID: https://orcid.org/0000-0001-7507-5267)
- Shubashini Rathina Velu (ORCID: https://orcid.org/0000-0002-7124-2010)
- V. M. Raja Sankari
- Rudrani Maity
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- Prince Mohammad bin Fahd University (SA)
- SRM Institute of Science and Technology (IN)
- Batangas State University (PH)
- Ministry of Health (SA)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-12-12
- DOI
- https://doi.org/10.3389/fmed.2024.1520878
- Citations
- 11
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.74
Funders
- King Salman Center for Disability Research