# Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation

arXiv: https://arxiv.org/abs/1904.07773 · HTML: https://mlsanity.com/paper/1904.07773/

## Key facts

- Cited by 107 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 30 of 107)

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- [Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data](https://mlsanity.com/paper/2210.00255/) (2022)
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- [Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease](https://mlsanity.com/paper/2303.07125/) (2023)
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- [Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning](https://mlsanity.com/paper/2106.01132/) (2021)
- [A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images](https://mlsanity.com/paper/2203.01944/) (2022)
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- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)
- [Reproducibility in machine learning for medical imaging](https://mlsanity.com/paper/2209.05097/) (2022)
- [How precise are performance estimates for typical medical image segmentation tasks?](https://mlsanity.com/paper/2210.14677/) (2022)
- [RegBN: Batch Normalization of Multimodal Data with Regularization](https://mlsanity.com/paper/2310.00641/) (2023)
- [TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis](https://mlsanity.com/paper/2407.06852/) (2024)
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- [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)
- [Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research](https://mlsanity.com/paper/2301.08525/) (2023)

## Abstract (arXiv)

Over 30 papers have proposed to use convolutional neural network (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review and found that more than half of the surveyed papers may have suffered from data leakage. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.