Article

DLNDD: An Explainable Deep Learning Framework for the Early Detection

2026-06-12

Abstract excerpt

<title>Abstract</title> <p>Background Rare Neurological Diseases (RNDs) affect over 300 million individuals globally, yet confirmed diagnosis requires five to seven years on average due to scarce clinical data, phenotypic heterogeneity, and limited specialist familiarity. Existing AI approaches address individual modalities in isolation; no unified framework simultaneously exploits MRI imaging, symptom text, gen...

Topics

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

Identifiers and source

Literature Corpus work: d1df5080-f5b0-517f-94aa-e073133bd170
DOI: 10.21203/rs.3.rs-9837234/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

DLNDD: An Explainable Deep Learning Framework for the Early DetectionDOI 10.21203/rs.3.rs-9837234/v1Read

DLNDD: An Explainable Deep Learning Framework for the Early Detection and Classification of Rare DiseasesSemantic proximity Read

From Data to Diagnosis: A Deep Dive into Deep Learning for COVID-19 DetectionSemantic proximity Read

COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-RaysSemantic proximity Read

Deep Learning for Rare Disease: A Scoping ReviewSemantic proximity Read

NeuroXNet: Creating A Novel Deep Learning Architecture that Diagnoses Neurological Disorders, Finds New Blood Biomarkers, and Assesses Surgical, Drugs, and Radiation Treatment Plans Using Medical Imaging and Genomic DataSemantic proximity Read

Health system learning achieves generalist neuroimaging modelsSemantic proximity Read

A State-of-the-Art Review on Multimodal Deep Learning for Medical Diagnosis: Integrating Text and ImageSemantic proximity Read

SLIViT: a general AI framework for clinical-feature diagnosis from limited 3D biomedical-imaging dataSemantic proximity Read

Disorder-Free Data are All You Need: Inverse Supervised Learning for Broad-Spectrum Head Disorder DetectionSemantic proximity Read

Explainable deep learning for disease activity prediction in chronic inflammatory joint diseasesSemantic proximity Read

Dynamic Preclinical Detection and Progression Prediction of Neurodegenerative Diseases Using Multi-Modal Deep LearningSemantic proximity Read

Reframing AI for Rare Disease RecognitionSemantic proximity Read