# Soft-Weighted CrossEntropy Loss for Continous Alzheimer's Disease Detection

arXiv: https://arxiv.org/abs/2402.11931 · HTML: https://mlsanity.com/paper/2402.11931/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0](https://mlsanity.com/paper/2501.16201/) (2025)

## Abstract (arXiv)

Alzheimer's disease is a common cognitive disorder in the elderly. Early and accurate diagnosis of Alzheimer's disease (AD) has a major impact on the progress of research on dementia. At present, researchers have used machine learning methods to detect Alzheimer's disease from the speech of participants. However, the recognition accuracy of current methods is unsatisfactory, and most of them focus on using low-dimensional handcrafted features to extract relevant information from audios. This paper proposes an Alzheimer's disease detection system based on the pre-trained framework Wav2vec 2.0 (Wav2vec2). In addition, by replacing the loss function with the Soft-Weighted CrossEntropy loss function, we achieved 85.45% recognition accuracy on the same test dataset.