DeepAD: Alzheimer’s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI

By Saman Sarraf, D. DeSouza, John A. E. Anderson, Ghassem Tofighi. 2016

366 citations recorded by the source corpus.

To extract patterns from neuroimaging data, various statistical methods and machine learning algorithms have been explored for the diagnosis of Alzheimer’s disease among older adults in both clinical and research applications; however, distinguishing between Alzheimer’s and healthy brain data has been challenging in older adults (age > 75) due to highly similar patterns of brain atrophy and image intensities. Recently, cutting-edge deep learning technologies have rapidly expanded into numerous fields, including medical image analysis. This paper outlines state-of-the-art deep learning-based pipelines employed to distinguish Alzheimer’s magnetic resonance imaging (MRI) and functional MRI (fMRI) from normal healthy control data for a given age group. Using these pipelines, which were executed on a GPU-based high-performance computing platform, the data were strictly and carefully preprocessed. Next, scale- and shift-invariant low- to high-level features were obtained from a high volume of training images using convolutional neural network (CNN) architecture. In this study, fMRI data were used for the first time in deep learning applications for the purposes of medical image analysis and Alzheimer’s disease prediction. These proposed and implemented pipelines, which demonstrate a significant improvement in classification output over other studies, resulted in high and reproducible accuracy rates of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively. Additionally, for clinical purposes, subject-level classification was performed, resulting in an average accuracy rate of 94.32% and 97.88% for the fMRI and MRI pipelines, respectively. Finally, a decision making algorithm designed for the subject-level classification improved the rate to 97.77% for fMRI and 100% for MRI pipelines.

Subfields: Cognitive Neuroscience, Neurology, Psychiatry and Mental health

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “3D MRI and 4D fMRI (with time as a dimension) data can be decomposed into sets of 2D images [367].”
A review of the application of deep learning in medical image classification and segmentation (2020, methodology): “(33) trained AD samples for sMRI and fMRI using the wellknown LeNet-5 framework in CNNs, respectively, yielding 98.”
A Survey of Deep Learning for Scientific Discovery (2020, background): “In semantic segmentation, all the pixels corresponding to balloon are identiﬁed, while in instance segmentation, each individual balloon is identiﬁed separately. pathology [135, 55], analyzing brain scans (PET, fMRI) [202, 45].”
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker (2016, background): “Recently, 3D convolutional neural networks have been also proposed for Alzheimer's disease classification (Payan and Montana, 2015; Sarraf and Tofighi, 2016), brain lesion segmentation (Kamnitsas et al., 2017) and skull stripping (Kleesiek et al., 2016).”
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks (2019, methodology): “Researchers have used various preprocessing steps on this dataset [14, 30].”
DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images (2021, positive): “Sarraf et al. [27] presented a deep learning pipeline where the CNN model is trained with many training images to perform feature classiﬁcation on the scale and shift-invariant processes.”
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Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, neutral): “…(CNNs), near-radiologist level performance can be achieved in automated medical image analysis tasks including classification of Alzheimer’s disease (Sarraf et al., 2017), skin lesions (Esteva et al., 2017; Kawahara and Hamarneh, 2016) and echo-cardiogram views (Madani et al., 2018), lung nodule…”
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