2020/11/28 by Fatih Altay, Guillermo Ramón Sánchez, Altay, Fatih +9 · 1 citation
Medicine · Neuroscience · #Brain Tumor Detection and Classification #Dementia and Cognitive Impairment Research #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2011.14139
openalex publication_date 2020/11/28 · openalex created_date 2022/09/08 · openalex updated_date 2026/07/28
Alzheimer's disease is one of the diseases that mostly affects older people\nwithout being a part of aging. The most common symptoms include problems with\ncommunicating and abstract thinking, as well as disorientation. It is important\nto detect Alzheimer's disease in early stages so that cognitive functioning\nwould be improved by medication and training. In this paper, we propose two\nattention model networks for detecting Alzheimer's disease from MRI images to\nhelp early detection efforts at the preclinical stage. We also compare the\nperformance of these two attention network models with a baseline model.\nRecently available OASIS-3 Longitudinal Neuroimaging, Clinical, and Cognitive\nDataset is used to train, evaluate and compare our models. The novelty of this\nresearch resides in the fact that we aim to detect Alzheimer's disease when all\nthe parameters, physical assessments, and clinical data state that the patient\nis healthy and showing no symptoms\n