This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network (MCENN)
Nov 24, 2022 · Convolutional neural networks (CNNs) have been increasingly used in the computer-aided diagnosis of Alzheimer's Disease (AD).
This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network (MCENN) by ...
Monte Carlo Ensemble Neural Network for the diagnosis of ...
www.researchgate.net › ... › Monte Carlo
Oct 22, 2024 · This study takes the advantage of the 2D-slice CNN fast computation and ensemble approaches to develop a Monte Carlo Ensemble Neural Network ( ...
Bäckström, K., Nazari, M., Gu, I. Y.-H., & Jakola, A. S. (2018). An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images.
This study introduces a novel approach, Monte Carlo Ensemble Vision Transformer (MC-ViT), which develops an ensemble approach with Vision transformer (ViT).
Jan 3, 2025 · Recently, deep neural network techniques have significantly advanced the classification of AD, overcoming the limitations of traditional manual ...
People also ask
How do they diagnose Alzheimer's disease?
What are the machine learning techniques for diagnosis of Alzheimer's disease?
What recommendations can be made for the health promotion of a patient diagnosed with Alzheimer's disease?
What is the pathology of Alzheimer's disease?
Convolutional neural networks (CNNs) have been increasingly used in the computer-aided diagnosis of Alzheimer's Disease (AD). This study takes the advantage of ...
By harnessing Monte Carlo sampling, this method produces a broad spectrum of classification decisions, enhancing the MC-ViT performance. This novel technique.
In this study, two deep neural network techniques, AlexNet and Restnet50, were applied for the classification and recognition of AD. The data used in this study ...