Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks
Year: 2018
Type: article
Abstract: Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer's disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer's disease diagnosis in clinical research. Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis. We conducted ample experiments to demonstrate that our proposed model outperformed comparative baselines on the Open Access Series of Imaging Studies dataset.
Source: Brain Informatics
Authors: Jyoti Islam, Yanqing Zhang
Institution: Georgia State University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,090
Awards: Mapping Abnormal Neurodevelopment in Schizophrenia, Functional-anatomic exploration of cognitive control, Morphometry Biomedical Informatics Research Network, Alzheimer's Disease Research Center, Healthy Aging and Senile Dementia, Antecedent Biomarkers for Alzheimer Disease: The Adult Children Study
OpenAlex ID: W2805494981