# A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images

arXiv: https://arxiv.org/abs/2203.01944 · HTML: https://mlsanity.com/paper/2203.01944/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform](https://mlsanity.com/paper/2107.05990/) (2021)
- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)
- [Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging](https://mlsanity.com/paper/1807.11228/) (2018)
- [Multi-scale Graph-based Grading for Alzheimer's Disease Prediction](https://mlsanity.com/paper/1907.06625/) (2019)

## Built on by (most cited first, 6 of 6)

- [Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs](https://mlsanity.com/paper/2212.14806/) (2022)
- [Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI](https://mlsanity.com/paper/2306.11837/) (2023)
- [Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images](https://mlsanity.com/paper/2403.15443/) (2024)
- [BEE-NET: A deep neural network to identify in-the-wild Bodily Expression of Emotions](https://mlsanity.com/paper/2402.13955/) (2024)
- [L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection](https://mlsanity.com/paper/2406.16913/) (2024)
- [A reproducible 3D convolutional neural network with dual attention module (3D-DAM) for Alzheimer's disease classification](https://mlsanity.com/paper/2310.12574/) (2023)

## Abstract (arXiv)

Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.