2015/02/09 by Adrien Payan, Payan, Adrien, Giovanni Montana +1 · 1 citation
Computer Science · Engineering · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Applications (stat.AP) #Computer Vision and Pattern Recognition (cs.CV) #Dementia and Cognitive Impairment Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Imaging and Analysis #cs.CV #cs.LG #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1502.02506
arxiv created 2015/02/09 · openalex publication_date 2015/02/09 · arxiv updated 2015/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.