Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks

• Published in: Brain informatics Vol. 5; no. 2; pp. 2 - 14
• Main Authors: Islam, Jyoti, Zhang, Yanqing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 31.05.2018; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Alzheimer's disease; Artificial Intelligence; Artificial neural networks; Brain damage; Brain imaging; Brain research; Cognitive Psychology; Computation by Abstract Devices; Computer Science; Convolutional neural network; Damage prevention; Data analysis; Deep learning; Diagnosis; Health Informatics; Human performance; Image analysis; Machine learning; Magnetic resonance imaging; Medical diagnosis; Medical imaging; Methods; MRI; Natural language processing; Neural networks; Neurological disorder; Neurosciences; NMR; Nuclear magnetic resonance; Original Research; Deep learning; Brain imaging; Alzheimer’s disease; Neurological disorder; MRI; Convolutional neural network
• ISSN: 2198-4018; 2198-4026
• DOI: 10.1186/s40708-018-0080-3

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.