Détail du document
Identifiant
oai:arXiv.org:2501.09999
Sujet
Computer Science - Computer Vision... Computer Science - Artificial Inte... Electrical Engineering and Systems...Auteur
Rafsan, Mohammad Oraby, Tamer Roy, Upal Kumar, Sanjeev Rodrigo, HansapaniCatégorie
Computer Science
Année
2025
Date de référencement
22/01/2025
Mots clés
disease mri computer alzheimerRésumé
Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function.
The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain.
Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients.
This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset.
Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data.
We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency.
This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.
Rafsan, Mohammad,Oraby, Tamer,Roy, Upal,Kumar, Sanjeev,Rodrigo, Hansapani, 2025, Deep Learning for Early Alzheimer Disease Detection with MRI Scans
Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation
cycle prediction framework feature design features life formation
