# Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease

arXiv: https://arxiv.org/abs/2006.14135 · HTML: https://mlsanity.com/paper/2006.14135/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Learning Models for Suicide Prediction from Social Media Posts](https://mlsanity.com/paper/2105.03315/) (2021)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation](https://mlsanity.com/paper/2111.08794/) (2021)
- [Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection](https://mlsanity.com/paper/2608.25028/) (2026)

## Abstract (arXiv)

In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer`s disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional ConvolutionalNeural Network (CNN) to generate two types of explanations of the model`s action: intra-class explanation and inter-class explanation. The inter-class explanation captures the relative importance of each of the different features in that class, while the inter-class explanation captures the relative importance between the classes. Note that although we have considered two classes of features in this paper, the architecture is easily expandable to more classes because of its modularity. Extensive experimentation and comparison with several recent models show that our method outperforms these methods with an accuracy of 92.2% and F1 score of 0.952on the DementiaBank dataset while being able to generate explanations. We show by examples, how to generate these explanations using attention values.