Article

Explainability of CNN-based Alzheimer’s Disease Detection from Online Handwriting

2024-04-18

Abstract excerpt

<title>Abstract</title> <p>With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer’s disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer’s disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack o...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: bb2cccd0-1839-5252-af3b-9c8173ff9d82
DOI: 10.21203/rs.3.rs-4215056/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Explainability of CNN-based Alzheimer’s Disease Detection from Online HandwritingDOI 10.21203/rs.3.rs-4215056/v1Read

Development of an interpretable deep learning system for the identification of patients with Alzheimer’s diseaseSemantic proximity Read

WITHDRAWN: A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural NetworksSemantic proximity Read

A Machine Learning Classification of Individuals with Mild Cognitive Impairment into Variants from WritingSemantic proximity Read

Review of Deep Learning Techniques for Neurological Disorders DetectionSemantic proximity Read

Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s DiseaseSemantic proximity Read

Explainable Artificial Intelligence to Diagnose Early Parkinson’s Disease via Voice AnalysisSemantic proximity Read

An Unsupervised XAI Framework for Dementia Detection with Context EnrichmentSemantic proximity Read

A Comparative Analysis of Parkinson’s Disease Diagnosis Approaches Using Drawing-Based Datasets: Utilizing Large Language Models, Machine Learning, and Fuzzy OntologiesSemantic proximity Read

Forecasting Alzheimer’s Disease Progression with Deep Multimodal Learning: Integration of 3D MRI and Tabular Clinical Records via a Large Vision-Language ModelSemantic proximity Read

Generative deep learning models for cognitive performance trajectories in real-world scenariosSemantic proximity Read

Using explainable artificial intelligence to identify linguistic biomarkers of amyloid pathology in primary progressive aphasiaSemantic proximity Read

Deep Learning Analysis of Figure Copying Tasks for Parkinson’s Disease Detection with GAN-Based Data AugmentationSemantic proximity Read