# Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data

arXiv: https://arxiv.org/abs/1905.00931 · HTML: https://mlsanity.com/paper/1905.00931/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 30 of 30)

- [Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond](https://mlsanity.com/paper/2103.10689/) (2021)
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- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)
- [Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability](https://mlsanity.com/paper/2103.16685/) (2021)
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- [Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research](https://mlsanity.com/paper/2301.08525/) (2023)
- [GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI](https://mlsanity.com/paper/2407.15719/) (2024)
- [ADAPT: Alzheimer Diagnosis through Adaptive Profiling Transformers](https://mlsanity.com/paper/2401.06349/) (2024)
- [MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes](https://mlsanity.com/paper/2302.11021/) (2023)
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- [Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data](https://mlsanity.com/paper/2104.04672/) (2021)
- [BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks](https://mlsanity.com/paper/2209.13096/) (2022)
- [Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis](https://mlsanity.com/paper/2306.05297/) (2023)
- [Leveraging Bi-Focal Perspectives and Granular Feature Integration for Accurate Reliable Early Alzheimer's Detection](https://mlsanity.com/paper/2407.10921/) (2024)
- [Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques](https://mlsanity.com/paper/2410.18972/) (2024)
- [Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods](https://mlsanity.com/paper/2501.15293/) (2025)
- [SSL-AD: Spatiotemporal Self-Supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets](https://mlsanity.com/paper/2509.10453/) (2025)
- [Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks](https://mlsanity.com/paper/2510.03351/) (2025)
- [A Multimodal Approach to Alzheimer's Diagnosis: Geometric Insights from Cube Copying and Cognitive Assessments](https://mlsanity.com/paper/2512.16184/) (2025)
- [Detecting Fetal Alcohol Spectrum Disorder in children using Artificial Neural Network](https://mlsanity.com/paper/2105.15074/) (2021)
- [An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization](https://mlsanity.com/paper/2107.13200/) (2021)
- [A layer-stress learning framework universally augments deep neural network tasks](https://mlsanity.com/paper/2111.08597/) (2021)
- [AI and Non AI Assessments for Dementia](https://mlsanity.com/paper/2307.01210/) (2023)
- [AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field](https://mlsanity.com/paper/2406.17822/) (2024)
- [Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomes](https://mlsanity.com/paper/2512.24901/) (2025)
- [Graph Attention Network-Based Detection of Autism Spectrum Disorder](https://mlsanity.com/paper/2603.26971/) (2026)
- [CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease](https://mlsanity.com/paper/2604.22428/) (2026)

## Abstract (arXiv)

Deep learning has shown outstanding performance in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without preprocessing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.