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A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease [article]

Kuo Yang, Emad A. Mohammed
2020 arXiv   pre-print
This review introduces various applications of modern AI algorithms in AD diagnosis, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Automatic Image Segmentation, Autoencoder  ...  , Graph CNN (GCN), Ensemble Learning, and Transfer Learning.  ...  Graph Convolutional Network (GCN) for AD Prediction X.  ... 
arXiv:2101.01781v1 fatcat:lqtovw4jlbdcjhh5rvresp2gmq

Front Matter: Volume 10572

Jorge Brieva, Juan David García, Natasha Lepore, Eduardo Romero
2017 13th International Conference on Medical Information Processing and Analysis  
.  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00, 01, 02, 03,  ...  Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  10572 17 Bone age detection via carpogram analysis using convolutional neural networks [10572-45] 10572 18 Automated detection of lung nodules with three-dimensional convolutional neural networks  ... 
doi:10.1117/12.2310208 dblp:conf/sipaim/X17 fatcat:5mpaoft6nfcwrbrauhmpsgzemy

A Novel Solution of an Elastic Net Regularization for Dementia Knowledge Discovery using Deep Learning [article]

Kshitiz Shrestha, Omar Hisham Alsadoon, Abeer Alsadoon, Tarik A. Rashid, Rasha S. Ali, P.W.C. Prasad, Oday D. Jerew
2021 arXiv   pre-print
and Aim: Accurate classification of Magnetic Resonance Images (MRI) is essential to accurately predict Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion.  ...  Methodology: The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularization.  ...  Besides, it provides accuracy, sensitivity, and specificity of the Convolutional Neural Network for the classification of Mild Cognitive Impairment conversion as 81.0%, 87.0%, and 84.2%, respectively.  ... 
arXiv:2109.00896v1 fatcat:m6o4bt5pjjhsbfayyjdyo522nm

Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment [chapter]

Daniel Stamate, Richard Smith, Ruslan Tsygancov, Rostislav Vorobev, John Langham, Daniel Stahl, David Reeves
2020 IFIP Advances in Information and Communication Technology  
Impairment (MCI), and Dementia (DEM), based on tuning three Deep Learning models: two Multi-Layer Perceptron (MLP1 and MLP2) models and a Convolutional Bidirectional Long Short-Term Memory (ConvBLSTM)  ...  The purpose of this paper is to conduct an analysis on ADNI data and determine its effectiveness for building classification models to differentiate the categories Cognitively Normal (CN), Mild Cognitive  ...  All of the models were able to recognize patterns differentiating the three classes DEM (Dementia), MCI (Minor Cognitive Impairment) and CN (Cognitive Normal), which indicates that each of these machine  ... 
doi:10.1007/978-3-030-49186-4_26 fatcat:cbq4fc64nbfsjf5d3a2vwsbsyi

Editorial: Intelligent Recognition and Detection in Neuroimaging

Yu-Dong Zhang, German Castellanos-Dominguez, Yuankai Huo, Juan Manuel Gorriz, Roohallah Alizadehsani
2022 Frontiers in Neuroscience  
Those PR methods include support vector machine, deep learning, transfer learning, convolutional neural network, graph neural network, attention neural network, explainable AI, trustworthy AI, etc.  ...  Intelligent recognition and detection use the latest pattern recognition (PR) methods to recognize and detect suspicious areas of neuroimaging scan images.  ...  We also thank the editors for their support for the publications of this Research Topic.  ... 
doi:10.3389/fnins.2022.943180 pmid:35864991 pmcid:PMC9294631 fatcat:s2lvsnw5jvexvcnjmm43ewdwnq

DEEP LEARNING FOR PRODUCTION SCALE BRAIN EXTRACTION IN MRI: RESULTS BASED ON LARGE DATASET TRAINING

Evan Fletcher, Alexander Knaack, Charles Decarli
2019 Alzheimer's & Dementia  
Here we demonstrate how FTP PET network measurements of tau spread differentiates probable Alzheimer's dementia (pAD), mild cognitive impairment (MCI), and cognitively unimpaired (CU) study participants  ...  From the undirected weighted graph created from the inter-regional distances, the global and local (efficiency, network strength) measurements of spread of tau in the network were calculated.  ... 
doi:10.1016/j.jalz.2019.06.4959 fatcat:r424njolafhhzculmasvdonx2y

An accurate Alzheimer's disease detection using a developed convolutional neural network model

Muhanad Tahrir Younis, Younus Tahreer Younus, Jamal Naser Hasoon, Ali Hussain Fadhil, Salama A. Mostafa
2022 Bulletin of Electrical Engineering and Informatics  
The proposed convolutional neural network (CNN) based detection model attained a high performance with an accuracy of 99.92%, considerably enhancing the results achieved via the pre-trained 16 layers in  ...  Alzheimer's disease indicates one of the highest difficult to heal diseases, and it is acutely affecting the elderly normal lives and their households.  ...  ACKNOWLEDGEMENTS "This paper was supported by Mustansiriyah University, Imam Ja'afar Al-Sadiq University, and the Center of Intelligent and Autonomous Systems (CIAS), Faculty of Computer Science and Information  ... 
doi:10.11591/eei.v11i4.3659 fatcat:4if7bmpbzffanb4jxmdjxaroiq

Detection of Autism Spectrum Disorder Using Graph Representation Learning Algorithms and Deep Neural Network, Based on fMRI Signals [article]

Ali Yousedian, Farzaneh Shayegh, Zeinab Maleki
2022 bioRxiv   pre-print
It is evident that the effect of graph embedding methods is making the connectivity matrix more suitable for applying to a deep network.  ...  The classifier adapted to the features embedded in graphs is a LeNet deep neural network.  ...  Also, Convolutional Neural Network (CNN) was used to effectively diagnose Alzheimer's disease (AD) (Sarraf et al., 2016) and mild cognitive impairment (MCI) (Meszlényi et al., 2017) .  ... 
doi:10.1101/2022.06.23.497324 fatcat:n6ykpiwdgjeq3cvnmov25ehc3m

Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI

Xiaowen Chen, Mingyue Tang, Aimin Liu, Xiaoqin Wei
2022 Annals of Translational Medicine  
We employed 3 cross-sectional data sets from the ADNI to conduct our binary-stratification [AD and normal controls (NCs), or AD and mild cognitive impairment (MCI)], and multi-stratification (AD, MCI,  ...  We proposed a deep convolutional neural network (CNN) and iterated random forest (RF) architecture for MRI image stratification by both anatomical location and image modality using the Alzheimer's Disease  ...  AD, Alzheimer's disease; MCI, mild cognitive impairment; NC, normal controls; MRI, magnetic resonance imaging; CNN, convolutional neural network.  ... 
doi:10.21037/atm-22-2961 pmid:35965800 pmcid:PMC9372697 fatcat:qrvvp4epvfcmnnlogw4s5oyypy

A Unified Framework for Personalized Regions Selection and Functional Relation Modeling for Early MCI Identification

Jiyeon Lee, Wonjun Ko, Eunsong Kang, Heung-Il Suk
2021 NeuroImage  
Furthermore, our method allows us to capture the functional relations of a subject-specific ROI subset through the use of a graph-based neural network.  ...  In this work, we propose a novel framework that can identify subjects with early-stage mild cognitive impairment (eMCI) and consider individual variability by learning functional relations from automatically  ...  Finally, the learned graph features are used in the classifier . ( : number of ROIs; CNN: convolution neural network; GCN: graph convolution network). 180 eMCI).  ... 
doi:10.1016/j.neuroimage.2021.118048 pmid:33878379 fatcat:djdqfurbxfc4nmdk3ghefiwwum

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes [article]

Emanuel A. Azcona, Pierre Besson, Yunan Wu, Arjun Punjabi, Adam Martersteck, Amil Dravid, Todd B. Parrish, S. Kathleen Bandt, Aggelos K. Katsaggelos
2020 arXiv   pre-print
This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which  ...  offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our  ...  be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimers disease (AD).  ... 
arXiv:2008.06151v3 fatcat:egbvln4dyvbtxbf4rqn5vs6rqm

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease

Rizwan Khan, Zahid Hussain Qaisar, Atif Mehmood, Ghulam Ali, Tamim Alkhalifah, Fahad Alturise, Lingna Wang
2022 Applied Sciences  
It is crucial to understand and detect AD at an early stage to slow down its progression due to the non-curable nature of the disease.  ...  The recent deep learning-based methods can contribute to the detection of the various stages of AD but require large-scale datasets and face several challenges while using the 3D volumes directly.  ...  Conflicts of Interest: The authors declare no conflict of interest. Appl. Sci. 2022, 12, 6507  ... 
doi:10.3390/app12136507 fatcat:mbrlqwyz6vbgbhj4oy7eaqdvt4

Sign Language Translator Using YOLO Algorithm [chapter]

Bhavadharshini M, Josephine Racheal J, Kamali M, Sankar S, Bhavadharshini M
2021 Advances in Parallel Computing  
This implemented system propounds an implementation of real time American Sign Language perception in Convolutional Neural Network (CNN) with the support of You Only Look Once version (YOLO) algorithm.  ...  Sign language is a terminology that encloses a motion of hand gestures which is an environment for the auditory impairment, individual (deaf or dumb) to deal with others.  ...  Convolution Neural Network is utilized to excavate the profound data of multi-layer organize within the handle of face acknowledgment.  ... 
doi:10.3233/apc210136 fatcat:asoizzxfcjacdmprqz5qooftje

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  [58] proposed a model consisting of dynamic spectral graph convolution networks (DS-GCNs) to predict early mild cognitive impairment (EMCI), and two assistive networks for gender and age to provide  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y
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