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

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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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]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hiram Zuniga, Ulises Orozco-Rosas, Kenia Picos ·

    A comparison of CNN architectures for Alzheimer's disease detection in single-view MRI scans

    arXiv:2608.11762v1 Announce Type: cross Abstract: Alzheimer's disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life. Diagnosis relies on medical history, cognitive tests, physical exams, and MRI b…

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