scholarly journals Structural‐MRI‐based Alzheimer's disease dementia score using 3D convolutional neural networks to achieve accurate early disease prediction

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Evangeline Yee ◽  
Karteek Popuri ◽  
Da Ma ◽  
Lei Wang ◽  
Mirza Faisal Beg
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
Structural Mri ◽  
Disease Prediction ◽  
Early Disease ◽  
Alzheimer’S Disease Dementia

scholarly journals Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06226
Author(s):  
Diedre Carmo ◽  
Bruna Silva ◽  
Clarissa Yasuda ◽  
Letícia Rittner ◽  
Roberto Lotufo
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
Hippocampus Segmentation

scholarly journals Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0230409 ◽  
Author(s):  
Eman N. Marzban ◽  
Ayman M. Eldeib ◽  
Inas A. Yassine ◽  
Yasser M. Kadah ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
Diffusion Tensor ◽  
Disease Diagnosis ◽  
Diffusion Tensor Images ◽  
Alzheimer’S Disease Diagnosis

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.

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