Alzheimer’s disease (AD) is a pressing global issue, demanding effective diagnostic approaches. This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning. Focusing on neuroimaging, this study explores single- and multi-modality investigations, delving into biomarkers, features, and preprocessing techniques. Various deep models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, are evaluated for their AD detection performance. Challenges such as limited datasets and training procedures persist. Emphasis is placed on the need to differentiate AD from similar brain patterns, necessitating discriminative feature representations. This review highlights deep learning’s potential and limitations in AD detection, underscoring dataset importance. Future directions involve benchmark platform development for streamlined comparisons. In conclusion, while deep learning holds promise for accurate AD detection, refining models and methods is crucial to tackle challenges and enhance diagnostic precision.

Alzheimer's disease (AD) poses a significant healthcare challenge, with an escalating prevalence and a forecasted surge in affected individuals. The urgency for precise diagnostic tools to enable early interventions and improved patient care is evident. Despite advancements, existing detection frameworks exhibit limitations in accurately identifying AD, especially in its early stages. Model optimisation and accuracy are other issues. This paper aims to address this critical research gap by introducing ConvADD, an advanced Convolutional Neural Network architecture tailored for AD detection. By meticulously designing ConvADD, this study endeavours to surpass the limitations of current methodologies and enhance accuracy metrics, optimisation, and reliability of AD diagnosis. The dataset was collected from Kaggle and consists of preprocessed 2D images extracted from 3D images. Through rigorous experimentation, ConvADD demonstrates remarkable performance metrics, showcasing its potential as a robust and effective. The proposed model shows remarkable results with a tool for AD detection accuracy of 98.01%, precision of 98%, recall of 98%, and an F1-Score of 98%, with only 2.1 million parameters. However, despite its promising results, several challenges and limitations remain, such as generalizability across diverse populations and the need for further validation studies. By elucidating these gaps and challenges, this paper contributes to the ongoing discourse on improving AD detection methodologies and lays the groundwork for future research endeavours in this domain.

Alzheimer’s disease (AD) affects over 50 million people worldwide, making early and accurate diagnosis essential for effective treatment and care planning. Diagnosing AD through neuroimaging continues to face challenges, including reliance on subjective clinical evaluations, the need for manual feature extraction, and limited generalisability across diverse populations. Recent advances in deep learning, especially convolutional neural networks (CNNs) and vision transformers, have improved diagnostic performance, but many models still depend on large labelled datasets and high computational resources. This study introduces an attention-enhanced CNN with a multi-activation fusion (MAF) module and evaluates it using the Alzheimer’s Disease Neuroimaging Initiative dataset. The channel attention mechanism helps the model focus on the most important brain regions in 3D MRI scans, while the MAF module, inspired by multi-head attention, uses parallel fully connected layers with different activation functions to capture varied and complementary feature patterns. This design improves feature representation and increases robustness across heterogeneous patient groups. The proposed model achieved 92.1% accuracy and 0.99 AUC, with precision, recall, and F1-scores of 91.3%, 89.3%, and 92%, respectively. Ten-fold cross-validation confirmed its reliability, showing consistent performance with 91.23% accuracy, 0.93 AUC, 90.29% precision, and 88.30% recall. Comparative analysis also shows that the model outperforms several state-of-the-art deep learning approaches for AD classification. Overall, these findings highlight the potential of combining attention mechanisms with multi-activation modules to improve automated AD diagnosis and enhance diagnostic reliability.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that severely affects memory and cognitive function, underscoring the need for accurate and early diagnostic tools. Deep learning has shown strong potential in automated AD detection from brain MRI scans; however, many existing approaches either overlook the full volumetric context of MRI data or rely on limited feature fusion strategies that weaken spatial coherence. To address these challenges, this study proposes BiFPN3DViT, a hybrid deep learning framework that integrates a hierarchical 3D convolutional backbone as the foundational component of a Bidirectional Feature Pyramid Network (BiFPN), coupled with a Vision Transformer (ViT) for global contextual learning. This study evaluated under leakage-safe subject-level splits on the ADNI dataset. The unified 3D CNN–BiFPN module jointly extracts and fuses multi-scale volumetric features through both bottom-up and top-down pathways, forming a rich spatial representation that is subsequently refined by the transformer encoder. Evaluated on 4,706 MRI scans from the Alzheimer’s Disease Neuroimaging Initiative ((ADNI) dataset, the proposed model achieved over 92% classification accuracy across AD, mild cognitive impairment (MCI), and cognitively normal (CN) subjects. Comparative and ablation studies confirm the complementary strengths of convolutional and transformer-based modelling, while attention visualisation highlights clinically relevant brain regions. These results demonstrate that BiFPN3DViT offers a scalable, interpretable, and computationally efficient framework for MRI-based Alzheimer’s disease diagnosis, advancing the integration of multi-scale feature learning and attention-driven analysis in neuroimaging.

Intradialytic hypotension (IDH) is a common complication in patients undergoing maintenance hemodialysis and is associated with an increased risk of cardiovascular and all-cause mortality. Machine learning (ML) and deep learning (DL) techniques transform healthcare by enabling accurate disease diagnosis, personalised treatment plans, and clinical decision support. However, challenges like data quality, privacy, and interpretability must be addressed for responsible adoption. This survey review aims to summarise and analyse relevant articles on applying machine learning models for predicting IDH. Among these models, deep learning, a subfield of machine learning, stands out because it can improve the overall performance of health care, particularly in diagnostic imaging and pathologic processes and in the synthetic judgment of big data flow. The insights gained from this survey review will assist researchers and practitioners in selecting appropriate machine-learning models and implementing preemptive measures to prevent IDH in dialysis patients.

Alzheimer’s disease (AD) classification from structural magnetic resonance imaging (MRI) remains challenging, particularly when distinguishing mild cognitive impairment (MCI) from both cognitively normal (CN) ageing and established AD. Multimodal approaches that combine imaging with clinical information are promising, but most confine the influence of clinical variables to the classifier head, so biomarkers cannot shape spatial feature extraction inside the imaging encoder. We propose a bimodal deep learning framework operating on two input sources: 3D T1-weighted structural MRI and tabular clinical assessment scores. A biomarker encoder maps the clinical scores into a conditioning representation that modulates vision transformer patch tokens through spatial rescaling and cross-attention throughout feature extraction. Within selected transformer layers, deformable biomarker attention (DBA) acts as an internal cross-modal modulation pathway rather than a third input modality, enabling sparse biomarker-guided spatial sampling from the 3D MRI feature grid. The framework was evaluated on ADNI data under strict subject-wise train, validation and test splits, so that repeated scans from the same individual never crossed partitions. On the held-out test set, the model achieved 95.68% accuracy, 95.39% macroprecision, 94.65% macrorecall and 95.01% macro F 1 score, exceeding all comparison methods reproduced under the same protocol. Prediction uncertainty was higher for misclassified cases, and rejecting the most uncertain cases raised retained set accuracy to 98.31% at 85.03% coverage. Performance degraded gradually rather than catastrophically when clinical scores were with-held, falling to 93.20% accuracy with all five biomarkers imputed. These results indicate that within-encoder biomarker conditioning improves AD classification and yields uncertainty-aware predictions that may support decision-making in clinical research settings.