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This study benchmarks ten convolutional neural network (CNN) architectures for detecting Alzheimer's disease using single-view MRI scans, employing a two-stage transfer learning and full fine-tuning approach on a class-balanced subset of the OASIS dataset. The evaluation reveals that VGG16 outperforms other architectures, achieving a validation accuracy of 0.9637 and a test accuracy of 0.9533. Notably, the research highlights the persistent challenge of distinguishing between Non-Demented and Very Mild Demented stages across all models tested.
VGG16 outshines other CNN architectures in Alzheimer's detection, but classifying early-stage dementia remains a significant hurdle.
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 brain scans, making deep learning suitable for Alzheimer's classification. This work proposes a benchmark that evaluates ten different convolutional neural network (CNN) architectures (including ResNet, DenseNet, MobileNet, EfficientNet, and VGG family models) under the same held-out test split protocol. A two-stage transfer learning and full fine-tuning pipeline is introduced to perform training using a class-balanced subset (3,900 images) derived from the OASIS medical imaging dataset, comprising 86,437 single-view MRI brain scans labeled into four classifications of Alzheimer's disease: Non-Demented, Very Mild Dementia, Mild Dementia, and Moderate Dementia. The best results were achieved by VGG16, with a 0.9637 validation accuracy and a 0.9533 test accuracy score. A key finding documented in this work is the difficulty of classifying the transition from Non-Demented to Very Mild Demented stages, observed consistently across all ten architectures.
