Deep learning feature fusion with transfer learning for Alzheimer’s disease classification

Deep learning feature fusion with transfer learning for Alzheimer’s disease classification. Download PDF Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by irreversible cognitive decline and memory impairment, for which early and accurate diagnosis is critical to enabling timely clinical intervention and disease management. Deep learning-based analysis of structural magnetic resonance imaging (MRI) has emerged as a promising approach for automated dementia staging, offering the ability to detect subtle neuroanatomical changes associated with disease progression that may not be easily identified through conventional clinical assessment.

This study investigates transfer learning and multimodel feature fusion for four-class Alzheimer’s disease severity classification using 6,400 T1 weighted MRI images obtained from a publicly available Kaggle dataset. The dataset includes four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. Three pretrained convolutional neural network architectures VGG16, ResNet50, and MobileNetV3 were independently fine tuned using ImageNet initialized weights.

To evaluate the effectiveness of feature-level integration, four fusion strategies feature concatenation, element-wise addition, element-wise multiplication, and attention-based fusion were implemented across three backbone pairings: VGG16-ResNet50, ResNet50-MobileNetV3, and VGG16-MobileNetV3. All models were trained for 60 epochs using the Adam optimizer and evaluated using accuracy, precision, recall, and F1-score. Among the standalone models, VGG16 achieved the highest performance, with a test accuracy of 99.48%, precision of 0.9929, recall of 0.9927, and F1-score of 0.9927.

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