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

By Jyoti Islam, Yanqing Zhang. 2018

403 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Daniel Rueckert
2. Ronald M. Summers
3. Christos Davatzikos
4. Hayit Greenspan
5. Jerry L Prince
6. James S. Duncan
7. Anant Madabhushi
8. S. Kevin Zhou

In Computer Vision and Pattern Recognition

1. B. van Ginneken
2. Daniel Rueckert
3. Christos Davatzikos
4. Ronald M. Summers
5. Olivier Colliot
6. Jorge Samper-González
7. Elina Thibeau-Sutre
8. Hayit Greenspan

In Neurology

1. Daniel Rueckert
2. Christos Davatzikos
3. B. van Ginneken

Citations in the corpus

• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “, 2018) or an imbalanced metric was computed on a severely imbalanced dataset (one class is less than half of the other) ​(Islam and Zhang, 2018; Qiu et al., 2018)​.”
• A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “In recent years, a large amount of work has been published on deep learning based prediction tasks such as brain age prediction [124], [125], [126], Alzheimer’s disease classification and trajectory modeling [127], [128], [129], [130], and schizophrenia classification [131], [132].”
• Deep Learning for Medical Anomaly Detection – A Survey (2020, result): “In contrast to the above approach which employs feature level fusion, an architecture using decision level fusion in proposed in [58].”
• A Review on a Deep Learning Perspective in Brain Cancer Classification (2019, neutral): “(a) Normal Brain [AtheroPointTM]; (b) Multiple Sclerosis [113]; (c) Stroke [114]; (d) Leukoaraiosis [115]; (e) Alzheimer’s Disease [116]; (f) Parkinson’s Disease [117]; (g) Wilson’ Disease [118]; ( ) Brain Tumor [119].”
• Nanomedicine-Driven Modulation of the Gut–Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer’s and Parkinson’s Disease (2025, unknown): “ResNet, which was popularized by its residual learning for training deep networks, has also been very successful in AD classification [119,120].”