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Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease [article]

Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter
2018 pre-print
In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain.  ...  Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems.  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense  ... 
doi:10.1007/978-3-030-02628-8_3 arXiv:1808.02874v1 fatcat:r45j2dsfpngwxlqr3p2jwuujvm

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.  ...  Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis.  ...  Architecture of Deep 3D Convolutional Neural Networks The architecture of the deep 3D convolutional neural networks (3D-CNN) for Alzheimer's disease classification in this study are based on the network  ... 
pmid:30815203 pmcid:PMC6371279 fatcat:3tyqplujlnfqpdahs5ofk4irhq

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.  ...  Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis.  ...  Architecture of Deep 3D Convolutional Neural Networks The architecture of the deep 3D convolutional neural networks (3D-CNN) for Alzheimer's disease classification in this study are based on the network  ... 
arXiv:1803.02544v3 fatcat:og3h2kfqurdkrjpssntumz7ev4

Predict Alzheimer's disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations

Sreevani Katabathula, Qinyong Wang, Rong Xu
2021 Alzheimer's Research & Therapy  
We have recently developed DenseCNN, a lightweight 3D deep convolutional network model, for AD classification based on hippocampus magnetic resonance imaging (MRI) segments.  ...  Background Alzheimer's disease (AD) is a progressive and irreversible brain disorder. Hippocampus is one of the involved regions and its atrophy is a widely used biomarker for AD diagnosis.  ...  Acknowledgements We thank the Alzheimer's Disease Neuroimaging Initiative (ADNI) for generously sharing clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's  ... 
doi:10.1186/s13195-021-00837-0 pmid:34030743 fatcat:ujjf6osodzcnbnq3btnsp7z6am

Prediction of Alzheimer's Disease Based on Coordinate-Dense Attention Network [chapter]

Yongmei Tang, Xiangyun Liao, Weixin Si, Zhigang Ning
2021 Frontiers in Artificial Intelligence and Applications  
In this paper, an automatic disease prediction scheme based on MRI was designed. A dense convolutional network was used as the basic model.  ...  Structural magnetic resonance imaging (sMRI) can describe structural changes in the brain and provide a diagnostic method for the detection and early prevention of Alzheimer's disease.  ...  Acknowledgment This work is supported by multiple grants, including: The National Key Research and Development Program of China (2020YFB1313900), National Natural Science Foundation of China (61902386)  ... 
doi:10.3233/faia210390 fatcat:nkznkeiqc5azxjxdpdpag6ceju

An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI [article]

Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, Daoqiang Zhang
2020 arXiv   pre-print
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and  ...  In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from  ...  of Alzheimer's disease.  ... 
arXiv:2008.04024v1 fatcat:3g3j3qgkqfdchabdpymv6yale4

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)  
This paper describes our investigation of the classification accuracy based on two publicly available data sets, namely, ADNI and OASIS, by building a 3D VGG variant convolutional network (CNN).  ...  Alzheimer's Disease (AD) is a widespread neurodegenerative disease caused by structural changes in the brain and leads to deterioration of cognitive functions.  ...  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

MPC-STANet: Alzheimer's Disease Recognition Method Based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Yujian Liu, Kun Tang, Weiwei Cai, Aibin Chen, Guoxiong Zhou, Liujun Li, Runmin Liu
2022 Frontiers in Aging Neuroscience  
Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition  ...  Alzheimer's disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset.  ...  Lebedev et al. (2014) used a random forest classifier trained based on MRI measures of different structures for the diagnosis of Alzheimer's disease and achieve the best AD/HC sensitivity/specificity  ... 
doi:10.3389/fnagi.2022.918462 fatcat:chnnaxuxyzc3tn5hxlsv76mghu

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification

Shangran Qiu, Prajakta S Joshi, Matthew I Miller, Chonghua Xue, Xiao Zhou, Cody Karjadi, Gary H Chang, Anant S Joshi, Brigid Dwyer, Shuhan Zhu, Michelle Kaku, Yan Zhou (+9 others)
2020 Brain  
Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization  ...  of individual Alzheimer's disease risk en route to accurate diagnosis.  ...  Additional support was provided by Boston University's Affinity Research Collaboratives program and Boston University Alzheimer's Disease Center (P30-AG013846).  ... 
doi:10.1093/brain/awaa137 pmid:32357201 fatcat:gy62vvhwevccflc57zfjqbj7fy

Diagnosis of Alzheimer's Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network

Peng Zhang, Shukuan Lin, Jianzhong Qiao, Yue Tu
2021 Sensors  
It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer's disease. MRI data is widely used for disease diagnosis.  ...  In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21227634 pmid:34833710 pmcid:PMC8623279 fatcat:eyxsktithfevdil5pob24epxze

Machine Learning Based Approach for Detection of Alzheimer's Disease

Maskeen kaur, Amanjot kaur
2021 International Journal of Scientific and Research Publications (IJSRP)  
3D MRI data plays a very important role as it can come up with the better results for the proper diagnosis of the particular disease.  ...  paper, prediction of AD based on a deep neural network from magnetic resonance imaging (MRI) is proposed.Recognizing signs early as much as possible is important as disorder enhancing drugs might be best  ...  In this paper the gift a state of the artwork Deep Convolutional Neural Network to come across Alzheimer's Disease and Dementia from 3-D MRI picture.  ... 
doi:10.29322/ijsrp.11.11.2021.p11942 fatcat:cssn5miyurhvznn5wq7gague2i

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
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures.  ...  Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis.  ...  In [23, 30] , the use of MRI and PET imaging in multimodal convolutional neural networks (CNNs) for ADD diagnosis is discussed.  ... 
arXiv:2008.06151v3 fatcat:egbvln4dyvbtxbf4rqn5vs6rqm

3D Grid-Attention Networks for Interpretable Age and Alzheimer's Disease Prediction from Structural MRI [article]

Pradeep Lam, Alyssa H. Zhu, Iyad Ba Gari, Neda Jahanshad, Paul M. Thompson
2020 arXiv   pre-print
In evaluations based on 4,561 3-Tesla T1-weighted MRI scans from 4 phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI), salience maps for age and AD prediction partially overlapped, but lower-level  ...  The resulting visual analyses can distinguish interpretable feature patterns that are important for predicting clinical diagnosis.  ...  Our contributions are as follows: • We propose a new architecture for age prediction and Alzheimer's disease classification based on structural MRI. • Our method shows group differences between brain age  ... 
arXiv:2011.09115v1 fatcat:mcsx2qtrsfemtklmkywwanbv6u

Diagnosis of Alzheimer's Diseases from MRI Images using Image Processing and Machine Learning Approach

Vandana B.S., Sathyavathi R. Alva
2021 International Journal of Computer Applications  
Alzheimer disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer's disease can help with proper treatment and prevent brain tissue damage.  ...  Radiological feature extraction using image processing and machine learning from MRI images and Analysis of Alzheimer'sdiseases state by using deep learning approach.  ...  The dovetailing pursuit was attempted in the computer aided diagnosis for Alzheimer's disease.  ... 
doi:10.5120/ijca2021921641 fatcat:rqli2nsuqfeinai3jwggnwobyy

Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks

Jyoti Islam, Yanqing Zhang
2018 Brain Informatics  
We propose a deep convolutional neural network for Alzheimer's disease diagnosis using brainMRI data analysis.  ...  Detection of Alzheimer's disease is exacting due to the similarity in Alzheimer's disease MRI data and standard healthy MRI data of older people.  ...  Recently, physicians are using brain MRI for Alzheimer's disease diagnosis. AD shrinks the hippocampus and cerebral cortex of the brain and enlarges the ventricles [2] .  ... 
doi:10.1186/s40708-018-0080-3 pmid:29881892 fatcat:zybwvmkiwfh5bedzaisbooxbry
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