Deep Convolutional Neural Networks for Automated Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Using 3D Brain MRI

2018 ◽  
pp. 359-369 ◽  
Author(s):  
Jyoti Islam ◽  
◽  
Yanqing Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Convolutional Neural Networks ◽  
Brain Mri ◽  
Automated Diagnosis ◽  
Deep Convolutional Neural Networks

scholarly journals Deep Learning in Brain MRI: Effect of Data Leakage Due to Slice-level Split Using 2D Convolutional Neural Networks

2021 ◽  
Author(s):  
Ekin Yagis ◽  
Selamawet Workalemahu Atnafu ◽  
Alba García Seco de Herrera ◽  
Chiara Marzi ◽  
Marco Giannelli ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
De Novo ◽  
Neurological Diseases ◽  
Brain Mri ◽  
Progression Markers ◽  
Magnetic Resonance Imaging Mri

Abstract In recent years, 2D convolutional neural networks (CNNs) have been extensively used for the diagnosis of neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models for the classification of patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson's Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.

scholarly journals Prediction and Modeling of Neuropsychological Scores in Alzheimer’s Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks

2022 ◽  
Vol 15 ◽  
Author(s):  
Seyed Hani Hojjati ◽  
Abbas Babajani-Feremi ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Artificial Neural Networks ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Angular Gyrus ◽  
Multimodal Neuroimaging ◽  
Non Linear ◽  
Neuroimaging Data

Background: In recent years, predicting and modeling the progression of Alzheimer’s disease (AD) based on neuropsychological tests has become increasingly appealing in AD research.Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data.Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer’s disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers.Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD.Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

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