Explainability of CNN-based Alzheimer’s disease detection from online handwriting

• Published in: Scientific reports Vol. 14; no. 1; pp. 22108 - 13
• Main Authors: Sweidan, Jana, El-Yacoubi, Mounim A., Rigaud, Anne-Sophie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 1D-CNN; 639/705; 692/308; 692/53; 692/699; Aged; Aged, 80 and over; Alzheimer Disease - diagnosis; Alzheimer Disease - physiopathology; Alzheimer's disease; Artificial Intelligence; Computer Science; Deep Learning; Dementia disorders; Disease detection; Explainability; Female; Handwriting; Humanities and Social Sciences; Humans; Learning algorithms; Life Sciences; Machine Learning; Male; Middle Aged; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neurons and Cognition; Online handwriting; Science; Science (multidisciplinary); 1D-CNN; Online handwriting; Alzheimer’s disease; Explainability; Alzheimer's disease; Alzheimer's disease Online handwriting 1D-CNN Explainability

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 widesprea...