Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
J Wen, E Thibeau-Sutre, M Diaz-Melo… - Medical image …, 2020 - Elsevier
Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries.
From a research point of view, impressive results have been reported using computer-aided …
From a research point of view, impressive results have been reported using computer-aided …
Deep learning based pipelines for Alzheimer's disease diagnosis: a comparative study and a novel deep-ensemble method
A Loddo, S Buttau, C Di Ruberto - Computers in biology and medicine, 2022 - Elsevier
Background Alzheimer's disease is a chronic neurodegenerative disease that destroys brain
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
cells, causing irreversible degeneration of cognitive functions and dementia. Its causes are …
Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis
Alzheimer's Disease (AD) is a chronic neurodegenerative disease without effective
medications or supplemental treatments. Thus, predicting AD progression is crucial for …
medications or supplemental treatments. Thus, predicting AD progression is crucial for …
Deep learning and neurology: a systematic review
AAA Valliani, D Ranti, EK Oermann - Neurology and therapy, 2019 - Springer
Deciphering the massive volume of complex electronic data that has been compiled by
hospital systems over the past decades has the potential to revolutionize modern medicine …
hospital systems over the past decades has the potential to revolutionize modern medicine …
Deep sequence modelling for Alzheimer's disease detection using MRI
Background Alzheimer's disease (AD) is one of the deadliest diseases in developed
countries. Treatments following early AD detection can significantly delay institutionalisation …
countries. Treatments following early AD detection can significantly delay institutionalisation …
Transfer learning for Alzheimer's disease through neuroimaging biomarkers: a systematic review
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since
2017, deep learning models with transfer learning approaches have been gaining …
2017, deep learning models with transfer learning approaches have been gaining …
Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder
In this paper, we propose a new method to perform data augmentation in a reliable way in
the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based …
the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based …
[Retracted] An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network
Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading
to a steady deterioration in cognitive ability. Deep learning models have shown outstanding …
to a steady deterioration in cognitive ability. Deep learning models have shown outstanding …
Transfer learning for Alzheimer's disease detection on MRI images
A Ebrahimi-Ghahnavieh, S Luo… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
In this paper, we focus on Alzheimer's disease detection on Magnetic Resonance Imaging
(MRI) scans using deep learning techniques. The lack of sufficient data for training a deep …
(MRI) scans using deep learning techniques. The lack of sufficient data for training a deep …