Alzheimer's diseases detection by using deep learning algorithms: a mini-review
S Al-Shoukry, TH Rassem, NM Makbol - IEEE Access, 2020 - ieeexplore.ieee.org
The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient
treatment, especially at the disease's early stages, because risk awareness allows the …
treatment, especially at the disease's early stages, because risk awareness allows the …
Volumetric feature-based Alzheimer's disease diagnosis from sMRI data using a convolutional neural network and a deep neural network
A Basher, BC Kim, KH Lee, HY Jung - IEEE Access, 2021 - ieeexplore.ieee.org
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is mostly
prevalent in people older than 65 years. The hippocampus is a widely studied region of …
prevalent in people older than 65 years. The hippocampus is a widely studied region of …
Single and combined neuroimaging techniques for Alzheimer's disease detection
Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality
of neuron connection in the brain, which consists of gradual synaptic integrity and loss of …
of neuron connection in the brain, which consists of gradual synaptic integrity and loss of …
Deep learning-based approach for multi-stage diagnosis of Alzheimer's disease
Alzheimer's Disease (AD) is a common neurological brain disorder that causes the brain
cells to die and shrink (Atrophy) gradually, resulting in a continuous decline in one's ability to …
cells to die and shrink (Atrophy) gradually, resulting in a continuous decline in one's ability to …
RP-Net: a 3D convolutional neural network for brain segmentation from magnetic resonance imaging
L Wang, C Xie, N Zeng - IEEE Access, 2019 - ieeexplore.ieee.org
Quantitative analysis of brain volume is quite significant for the diagnosis of brain diseases.
Accurate segmentation of essential brain tissues from 3D medical images is fundamental to …
Accurate segmentation of essential brain tissues from 3D medical images is fundamental to …
Automatic localization and discrete volume measurements of hippocampi from MRI data using a convolutional neural network
A Basher, BC Kim, KH Lee, HY Jung - IEEE Access, 2020 - ieeexplore.ieee.org
Automatic hippocampal volume measurement from brain magnetic resonance imaging
(MRI) is a crucial task and an important research area, especially in the study of …
(MRI) is a crucial task and an important research area, especially in the study of …
Content-based medical image retrieval using delaunay triangulation segmentation technique
S Kugunavar, CJ Prabhakar - Research Anthology on Improving …, 2023 - igi-global.com
This article presents a novel technique for retrieval of lung images from the collection of
medical CT images. The proposed content-based medical image retrieval (CBMIR) …
medical CT images. The proposed content-based medical image retrieval (CBMIR) …
[HTML][HTML] Detection of Alzheimer's Disease Using Deep Learning Models: A Systematic Literature Review
EM Mohammed, AM Fakhrudeen, O Alani - Informatics in Medicine …, 2024 - Elsevier
Alzheimer's disease (AD) is a progressive neurological disease considered the most
common form of late-stage dementia. Usually, AD leads to a reduction in brain volume …
common form of late-stage dementia. Usually, AD leads to a reduction in brain volume …
[PDF][PDF] Detection and Classification of Alzheimer's Disease by Employing CNN
SS Shastri, A Bhadrashetty, S Kulkarni - Int. J. Intell. Syst. Appl, 2023 - academia.edu
Alzheimer's illness is an ailment of mind which results in mental confusion, forgetfulness and
many other mental problems. It effects physical health of a person too. When treating a …
many other mental problems. It effects physical health of a person too. When treating a …
Brain tumor image retrieval via multitask learning
M Pisov, G Makarchuk, V Kostjuchenko… - arXiv preprint arXiv …, 2018 - arxiv.org
Classification-based image retrieval systems are built by training convolutional neural
networks (CNNs) on a relevant classification problem and using the distance in the resulting …
networks (CNNs) on a relevant classification problem and using the distance in the resulting …