VGG16 leads Alzheimer's detection in MRI scans across ten CNNs

Researchers have benchmarked ten different convolutional neural network (CNN) architectures for detecting Alzheimer's disease from single-view MRI scans. The study utilized a transfer learning and fine-tuning pipeline on a subset of the OASIS dataset, comprising 3,900 images from 86,437 scans. VGG16 achieved the highest validation accuracy of 0.9637 and test accuracy of 0.9533. A consistent challenge across all tested architectures was distinguishing between the Non-Demented and Very Mild Dementia stages. AI

IMPACT This research provides a benchmark for AI models in medical imaging, potentially improving early detection of Alzheimer's disease.

RANK_REASON The cluster describes a research paper comparing machine learning models for a specific medical application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Alzheimer's disease
• convolutional neural network
• DenseNet
• EfficientNet
• MobileNet
• MRI scans
• OASIS medical imaging dataset
• ResNet
• VGG16
• VGG family models

• paper
• other

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