Enhancing Alzheimer's Disease Detection with Brain MRI: A Deep Learning Approach Optimized by Snow Ablation

Alzheimer's disease (AD) is a neurodegenerative condition marked by progressive cognitive decline, with early detection being vital for effective intervention. Recent advancements in artificial intelligence, particularly deep learning (DL) and machine learning (ML), have significantly improved the accuracy of AD detection using brain MRI data. This review synthesizes recent approaches, including web-based diagnostic platforms (e.g., CcADD), ensemble learning techniques leveraging structural biomarkers, and innovative models like brain-region attention networks within hybrid federated learning frameworks for multi-center data. Studies also explore the efficacy of multiclass classification using 3D MRI and the application of deep CNNs for robust feature extraction. Despite these advancements, challenges such as data heterogeneity, model generalization across clinical settings, and interpretability persist. Ongoing research emphasizes scalable, explainable, and clinically viable solutions to bridge the gap between automated diagnostics and practical healthcare deployment. This work provides an overview of current progress and future directions in AI-driven AD detection using neuro imaging.