Abstract:
Abstract
General Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder requiring accurate and accessible diagnostic support. Specific Background: Magnetic Resonance Imaging (MRI) is widely used for structural brain assessment, and Convolutional Neural Networks (CNN) enable automated feature extraction from medical images. Knowledge Gap: Prior studies report high classification performance but rarely integrate comprehensive evaluation with real-time deployment for decision support. Aims: This study develops and evaluates a CNN-based model for classifying 2D axial MRI images into Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and Common Normal (CN), alongside web-based implementation. Results: Using approximately 5,000 ADNI MRI images, the model achieved 97.67% accuracy, 97.73% precision, 97.67% recall, and 97.65% F1-score, with AUC values near 1.00. Learning curves indicated stable convergence without overfitting or underfitting, and confusion matrix analysis confirmed consistent multi-class discrimination. The deployed Hugging Face–Gradio application generated predictions in under five seconds per scan without performance degradation. Novelty: This research combines rigorous multi-metric validation with interactive web deployment as an artificial intelligence decision support system for early AD screening. Implications: The findings demonstrate the technical feasibility of CNN-based MRI classification for preliminary cognitive disorder screening, while emphasizing the need for multimodal integration and prospective clinical validation.
Highlights
• Achieved robust multi-class discrimination among AD, MCI, and CN categories using axial brain scans.
• Demonstrated stable training dynamics validated through loss convergence and receiver operating characteristics.
• Implemented an interactive artificial intelligence platform with sub-five-second prediction time.
Keywords
Alzheimers Disease; Convolutional Neural Networks; MRI Classification; Deep Learning; Clinical Decision Support System
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References
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