Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review

Purpose: Alzheimer's disease (AD) is a severe neurological disease that significantly impairs brain function. Timely identification of AD is essential for appropriate treatment and care. This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging for AD diagnosis, where popular imaging types, reviews well-known online accessible data sets, and describes different algorithms used in DL for the correct initial evaluation of AD are presented. Significance: Conventional diagnostic techniques, including medical evaluations and cognitive assessments, usually not identify the initial stages of Alzheimer's. Neuroimaging methods, when integrated with DL techniques, have demonstrated considerable potential in enhancing the diagnosis and categorization of AD. DL models have received significant interest due to their capability to identify AD in its early phases automatically, which reduces the mortality rate and treatment cost of AD. Method: An extensive literature search was performed in leading scientific databases, concentrating on papers published from 2021 to 2025. Research leveraging DL models on different neuroimaging techniques such as magnetic resonance imaging (MRI), positron emission tomography, and functional magnetic resonance imaging (fMRI), and so forth. The review complies with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Current developments show that CNN-based techniques, especially those utilizing hybrid and transfer learning frameworks, outperform conventional DL methods. Research employing the combination of multimodal neuroimaging data has demonstrated enhanced diagnostic precision. Still, challenges such as method interpretability, data heterogeneity, and limited data exist as significant issues. Conclusion: DL has considerably improved the accuracy and reliability of AD diagnosis with neuroimaging. Regardless of issues with data accessibility and adaptability, current studies into the interpretability of models and multimodal fusion provide potential for clinical application. Further research should concentrate on standardized data sets, rigorous validation architectures, and understandable AI methodologies to enhance the effectiveness of DL methods in AD prediction.