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Alzheimer's Disease Classification Using 2D Convolutional Neural Networks [article]

Xin Xing, Liangliang Liu, Qi Yin, Gongbo Liang
2021 medRxiv   pre-print
Convolutional neural networks with 3D kernels (3D CNNs) are often the default choice for deep learning based MRI analysis. However, 3D CNNs are usually computationally costly and data-hungry.  ...  We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset across two popular 2D CNN architectures.  ...  Convolutional neural networks (CNN), as a promising tool, are rapidly applied in the medical imaging domain recently [4] - [11] .  ... 
doi:10.1101/2021.05.24.21257554 fatcat:cheo65gv4fhtjmre7prekcdcte

Alzheimer's Detection through 3D Convolutional Neural Networks

Ryan Hogan, Christoforos Christoforou
2021 Proceedings of the ... International Florida Artificial Intelligence Research Society Conference  
In this study, we aim to test the feasibility of using three-dimensional convolutional neural networks to identify neurophysiological degeneration in the entire-brain scans that differentiate between AD  ...  The advancement of deep learning within biomedical imaging, particularly in MRI scans, has proven to be an efficient resource for abnormality detection while utilizing convolutional neural networks (CNN  ...  Convolutional Neural Networks The CNN has displayed tremendous success as an image recognition tool that specializes in feature detection and can outperform its predecessors such as feedback neural networks  ... 
doi:10.32473/flairs.v34i1.128476 fatcat:2lmdtcaz7rfhnexwx54kzjoeqm

Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks [article]

Saman Sarraf, Ghassem Tofighi
2016 arXiv   pre-print
In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain.  ...  Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer's subjects from normal controls where the accuracy of test data  ...  In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain.  ... 
arXiv:1603.08631v1 fatcat:eb636qmpxjco5dhvozmwe6x5fm

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

Chengliang Yang, Anand Rangarajan, Sanjay Ranka
2018 AMIA Annual Symposium Proceedings  
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification.  ...  The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.  ...  for natural image classification. 3D-CNNs for Alzheimer's Disease Classification There are two major methods for using 3D convolutional neural networks for Alzheimer's disease classification from brain  ... 
pmid:30815203 pmcid:PMC6371279 fatcat:3tyqplujlnfqpdahs5ofk4irhq

Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network

Fanar E. K. Al-Khuzaie, Oguz Bayat, Adil D. Duru, Mohammed Yahya Alzahrani
2021 Applied Bionics and Biomechanics  
We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet).  ...  Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network.  ...  Conclusions In order to diagnose Alzheimer's disease, deep neural networks, especially CNNs, can provide meaningful information.  ... 
doi:10.1155/2021/6690539 pmid:33623535 pmcid:PMC7872776 fatcat:mqiinerb5zainl7bg3x4hgpgoe

Understanding 3D CNN Behavior for Alzheimer's Disease Diagnosis from Brain PET Scan [article]

Jyoti Islam, Yanqing Zhang
2019 arXiv   pre-print
In this paper, we consider this issue and work on visualizing and understanding the decision of Convolutional Neural Network for Alzheimer's Disease (AD) Diagnosis.  ...  We develop a 3D deep convolutional neural network for AD diagnosis using brain PET scans and propose using five visualizations techniques - Sensitivity Analysis (Backpropagation), Guided Backpropagation  ...  But there is a lack of such work for medical image analysis using convolutional neural networks.  ... 
arXiv:1912.04563v2 fatcat:5tyyuzpxcbgydlblxhgd75jooq

3D Convolutional Neural Networks for Diagnosis of Alzheimer's Disease via Structural MRI

Ekin Yagis, Luca Citi, Stefano Diciotti, Chiara Marzi, Selamawet Workalemahu Atnafu, Alba G. Seco De Herrera
2020 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)  
We used 3D models to avoid information loss, which occurs during the process of slicing 3D MRI into 2D images and analyzing them by 2D convolutional filters.  ...  The proposed model achieved 73.4% classification accuracy on ADNI and 69.9% on OASIS dataset with 5-fold cross-validation (CV), outperforming 2D network models.  ...  ACKNOWLEDGMENT Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (  ... 
doi:10.1109/cbms49503.2020.00020 dblp:conf/cbms/YagisCDMAH20 fatcat:ifg2ysevyjgs3ppjevtakc6weq

Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks [article]

Adrien Payan, Giovanni Montana
2015 arXiv   pre-print
In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on  ...  Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years.  ...  RESULTS The convolutional neural networks using 2D and 3D convolutions were trained used a training set of 1 Table 1 : Review of selected methods for the AD and MCI classification.  ... 
arXiv:1502.02506v1 fatcat:ck7h3upmeng2zlournxt5nfqsu

A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks [article]

Ziqiang Guan and Ritesh Kumar and Yi Ren Fung and Yeahuay Wu and Madalina Fiterau
2019 arXiv   pre-print
A plethora of deep learning models have been developed for the task of Alzheimer's disease classification from brain MRI scans.  ...  <0.5%) improvement in model performance, (3) most popular convolutional neural network models yield similar performance when compared to each other.  ...  Deepad: Alzheimer s disease classification via deep convolutional neural networks using mri and fmri. BioRxiv, page 070441, 2017. Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna.  ... 
arXiv:1904.07950v1 fatcat:hj5q6h4g7zhrzoiw227skgfmvu

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification [article]

Chengliang Yang, Anand Rangarajan, Sanjay Ranka
2018 arXiv   pre-print
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification.  ...  The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.  ...  for natural image classification. 3D-CNNs for Alzheimer's Disease Classification There are two major methods for using 3D convolutional neural networks for Alzheimer's disease classification from brain  ... 
arXiv:1803.02544v3 fatcat:og3h2kfqurdkrjpssntumz7ev4

Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification [article]

Sergey Korolev, Amir Safiullin, Mikhail Belyaev, Yulia Dodonova
2017 arXiv   pre-print
In this paper, we show how similar performance can be achieved skipping these feature extraction steps with the residual and plain 3D convolutional neural network architectures.  ...  We demonstrate the performance of the proposed approach for classification of Alzheimer's disease versus mild cognitive impairment and normal controls on the Alzheimer's Disease National Initiative (ADNI  ...  We choose to use convolutional neural networks for their ability to tackle the two problems stated above.  ... 
arXiv:1701.06643v1 fatcat:y4sbilbxgfevbicrbdw5efs2va

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics [article]

Alexander Khvostikov, Karim Aderghal, Andrey Krylov, Gwenaelle Catheline, Jenny Benois-Pineau
2018 arXiv   pre-print
Then we propose our own design of a 3D Inception-based Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics.  ...  The comparison with the conventional AlexNet-based network using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstrates significantly better performance  ...  Ortiz-Surez in [42] explored the brain regions most contributing to Alzheimer's Disease by applying 2D convolutional neural networks to 2D sMRI brain images (coronal, sagittal and axial cuts).  ... 
arXiv:1809.03972v1 fatcat:b73nfo4cmjhetglwuwaorfehpa

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  
Patients who have Alzheimer's disease (AD) pass through several irreversible stages, which ultimately result in the patient's death.  ...  In this work, we propose a deep learning-based multiclass classification method to distinguish amongst various stages for the early diagnosis of Alzheimer's.  ...  Another convolutional neural network-based Alzheimer's disease classification from MRI brain data (CNN-AD) is proposed in [7] .  ... 
doi:10.3390/app12136507 fatcat:mbrlqwyz6vbgbhj4oy7eaqdvt4

Prediction of Alzheimer\'s Disease Using CNN

Kavya M K, Geetha M
2022 International Journal for Research in Applied Science and Engineering Technology  
We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brain MRI data analysis.  ...  While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer's disease and obtains superior performance for early-stage diagnosis.  ...  Therefore objectives of proposed system are as follows: 1) To develop a deep convolutional neural network that can identify Alzheimer's disease and classify the current disease stage. 2) To develop a network  ... 
doi:10.22214/ijraset.2022.45357 fatcat:rsq3rnhftrdxbgujoa2jx4fjiu

Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network

Ahila A, Poongodi M, Mounir Hamdi, Sami Bourouis, Kulhanek Rastislav, Faizaan Mohmed
2022 Frontiers in Public Health  
A novel and enhanced CAD system based on a convolutional neural network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients.  ...  Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment.  ...  The prime objective of this paper is to develop a robust classification system for AD diagnosis using a convolutional neural network (CNN).  ... 
doi:10.3389/fpubh.2022.834032 pmid:35198526 pmcid:PMC8860231 fatcat:72pskql5krbhvk2nubu3azeoxy
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