Cross-Dataset Generalisation of Machine Learning and Deep Learning for Three-Class Alzheimer’s Disease Staging: A Six-Model Benchmark Including a Bidirectional State-Space Model

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, and its pathology accumulates for years before symptoms appear, which places the mild cognitive impairment (MCI) stage at the centre of early detection. Machine learning (ML) and deep learning (DL) applied to structural magnetic resonance imaging (MRI) report high classification accuracy, yet these figures are usually measured within a single cohort and are rarely tested for transfer to an independent cohort, so whether they translate into clinical usefulness remains unresolved. The objective of this study is to address that gap directly. Six classifiers, three classical learners (Extra Trees, LightGBM and CatBoost) and three deep networks (ConvNeXt, CoAtNet and an introduced bidirectional Vision Mamba model, MambAD) were trained and tested on identical hippocampal slices from three publicly available datasets, namely ADNI, OASIS and a public Kaggle collection, for the three-class staging of cognitively normal (CN), MCI and AD. Every model was evaluated within cohorts and, more importantly, across cohorts, with subject-level scoring on the two clinical datasets. Within the primary ADNI cohort, the introduced model reached the highest balanced accuracy (BAC) at 0.578, although this lead was not statistically significant; across the six models, none dominated on every task, and no pairwise difference between models reached significance. BAC fell from about 0.55 within-cohort to between 0.38 and 0.45 across cohorts for every model, and the deep models transferred better on the ranking metric than the classical learners. The results show that a lightweight state-space model can match far larger pretrained backbones under matched conditions, and they confirm that single cohort accuracy overstates readiness, with cross-dataset generalisation remaining the binding constraint on clinical use.