# On the design of convolutional neural networks for automatic detection of Alzheimer's disease

arXiv: https://arxiv.org/abs/1911.03740 · HTML: https://mlsanity.com/paper/1911.03740/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

## Built on by (most cited first, 12 of 12)

- [Avoiding spurious correlations via logit correction](https://mlsanity.com/paper/2212.01433/) (2022)
- [Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model](https://mlsanity.com/paper/2108.09451/) (2021)
- [3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography](https://mlsanity.com/paper/2502.02779/) (2025)
- [SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training](https://mlsanity.com/paper/2210.04525/) (2022)
- [Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease](https://mlsanity.com/paper/2207.04574/) (2022)
- [TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis](https://mlsanity.com/paper/2407.06852/) (2024)
- [XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning](https://mlsanity.com/paper/2207.13223/) (2022)
- [Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling](https://mlsanity.com/paper/2311.16361/) (2023)
- [Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans](https://mlsanity.com/paper/2311.18245/) (2023)
- [A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification](https://mlsanity.com/paper/2604.26807/) (2026)
- [Deep Joint Learning of Pathological Region Localization and Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2108.04555/) (2021)
- [Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham](https://mlsanity.com/paper/2205.02885/) (2022)

## Abstract (arXiv)

Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural networks (CNNs) trained to detect AD using structural brain MRI scans. Specifically, we provide evidence that (1) instance normalization outperforms batch normalization, (2) early spatial downsampling negatively affects performance, (3) widening the model brings consistent gains while increasing the depth does not, and (4) incorporating age information yields moderate improvement. Together, these insights yield an increment of approximately 14% in test accuracy over existing models when distinguishing between patients with AD, mild cognitive impairment, and controls in the ADNI dataset. Similar performance is achieved on an independent dataset.