Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique
importance 7/10
ai assisted
Dementia and Cognitive Impairment Research neurology / medical imaging stage: research
Problem
Alzheimer's disease has no cure, so detecting it early from MRI scans and distinguishing its stages is critical but difficult.
Why it matters
Early detection of a currently incurable, progressive brain disease directly affects patient outcomes.
What the AI did
A DenseNet-201 transfer learning model with data augmentation classified MRI scans into five Alzheimer's stages (non-demented to severe).
Results
98.24% classification accuracy on the MRI dataset
Impact
High accuracy on one MRI dataset shown; no external validation or clinical deployment stated.
Record
| Venue | PLoS ONE |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Zia-ur-Rehman, Mohd Khalid Awang, Javed Rashid, Ghulam Ali, Muhammad Hamid, Samy Farouk Mahmoud, Dalia I. Saleh, Hafiz Ishfaq Ahmad |
| Citations | 30 (OpenAlex, at import time) |
| References | 42 |
| Model or method | DenseNet-201 (transfer learning) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1371/journal.pone.0304995 |
| Open access | gold |
| Keywords | Alzheimer's disease, MRI, DenseNet-201, transfer learning, classification |
Importance rating
7
significant — thousands to millions affected
Abstract states AD is incurable and early detection is critical; stage classification from MRI aids diagnosis of a major neurodegenerative disease, though no scale or clinical validation is given.