mlsanityData to 7 Oct 2026

Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework

arXiv 2605.04079 ↗First posted 14 Apr 2026cs.LG cs.AIcited by 0

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports the evaluation of emerging therapeutic strategies. In this paper, we propose a Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework for Alzheimer's disease diagnosis based on handwriting analysis. Handwriting signals provide a non-invasive and scalable digital biomarker that captures subtle cognitive-motor impairments associated with early AD progression. The proposed architecture allows multiple experts to specialize in different handwriting patterns while sharing a common base network. This design enables efficient learning of general representations while reducing interference between experts. Each expert is equipped with lightweight low-rank adapters. This mechanism significantly reduces the number of trainable parameters compared with standard Mixture of Experts (MoE) models and improves training stability. The proposed framework is evaluated on the Diagnosis AlzheimeR WIth haNdwriting (DARWIN) dataset. Extensive experiments are conducted, including ablation studies on key architectural parameters such as hidden dimension size, number of experts, and LoRA rank. The method is compared with multilayer perceptron (MLP) and conventional MoE architectures. In addition, stacking ensemble strategies (StackMean and StackMax) are investigated to improve robustness and predictive performance. Experimental results show that the LoRA-MoE framework achieves powerful diagnostic performance while activating significantly fewer parameters during inference. These results highlight the potential of the proposed approach as an accurate and computationally efficient solution for handwriting-based Alzheimer's disease screening and digital health applications.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 22 of its 39 references that are in the map. First posted on arXiv 14 Apr 2026.

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22 of 39 references.

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  1. 2023When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications21 Oct

Other references · 21

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  3. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  5. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  6. 2024Mixtral of Expertscited by 1,785
  7. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  8. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  9. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 834
  10. 2022DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scalecited by 400
  11. 2023LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Compositioncited by 359
  12. 2023AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuningcited by 337
9 more
  1. 2022Unified Scaling Laws for Routed Language Modelscited by 249
  2. 2023ReLoRA: High-Rank Training Through Low-Rank Updatescited by 200
  3. 2023Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuningcited by 148
  4. 2022A Kernel-Based View of Language Model Fine-Tuningcited by 118
  5. 2023Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuningcited by 106
  6. 2023The Expressive Power of Low-Rank Adaptationcited by 100
  7. 2024MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learningcited by 63
  8. 2024Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Modelscited by 38
  9. 2021A Machine Learning Model for Early Detection of Diabetic Foot using Thermogram Imagescited by 3

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