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Towards Alzheimer's Disease Classification through Transfer Learning [article]

Marcia Hon, Naimul Khan
2017 arXiv   pre-print
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years.  ...  In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets  ...  An attractive alternative to training from scratch is finetuning a deep network (especially CNN) through transfer learning [6] .  ... 
arXiv:1711.11117v1 fatcat:lxhc2ymkqvgjvidhk3fh2dx5mu

Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer's disease

2020 International Journal of Engineering and Advanced Technology  
Addressing the same, this article presents a systematic literature review of machine learning techniques developed for early diagnosis of Alzheimer's disease.  ...  Despite of the development of numerous machine learning models for early diagnosis of Alzheimer's disease, it is observed that there is a lot more scope of research.  ...  [39] directly came with an idea of a book multi-domain transfer learning model (MDTL). Deep transfer learning is used by Hon et al.  ... 
doi:10.35940/ijeat.c6508.029320 fatcat:wzyju5k4wrhgtdcc5h74bqjwve

Transfer Learning Assisted Classification and Detection of Alzheimer's Disease Stages Using 3D MRI Scans

Maqsood, Nazir, Khan, Aadil, Jamal, Mehmood, Song
2019 Sensors  
For early detection of Alzheimer's through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having  ...  The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet.  ...  However, transfer learning has never been used to test the accuracy of the system, which serves as a novel approach towards Alzheimer's classification.  ... 
doi:10.3390/s19112645 pmid:31212698 pmcid:PMC6603745 fatcat:73rdfdcx5nemfhx3iyw4725gz4

Alzheimer Disease Detection Techniques and Methods: A Review

Sitara Afzal, Muazzam Maqsood, Umair Khan, Irfan Mehmood, Hina Nawaz, Farhan Aadil, Oh-Young Song, Yunyoung Nam
2021 International Journal of Interactive Multimedia and Artificial Intelligence  
Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification.  ...  The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer's disease.  ...  III Machine Learning-based Approaches for classification of Normal Control vs Alzheimer's Disease The main idea of our multi-domain transfer learning-based method is to exploit the multi-auxiliary domain  ... 
doi:10.9781/ijimai.2021.04.005 fatcat:yklogr5wefei7e247dmjdrosum

Binary Classification of Alzheimer Disease using sMRI Imaging modality and Deep Learning [article]

Ahsan Bin Tufail, Qiu-Na Zhang, Yong-Kui Ma
2020 Journal of digital imaging   accepted
Experimental results show that the transfer learning approaches exceed the performance of non-transfer learning based approaches demonstrating the effectiveness of these approaches for the binary AD classification  ...  Alzheimer's disease (AD) is an irreversible devastative neurodegenerative disorder associated with progressive impairment of memory and cognitive functions.  ...  In this work, we have used Xception and Inception version 3 neural network models as transfer learning models for the classification of subjects between the Alzheimer's and non-Alzheimer's groups.  ... 
doi:10.1007/s10278-019-00265-5 pmid:32728983 pmcid:PMC7573078 arXiv:1809.06209v2 fatcat:ffiwfrekwrhlnct3qqcc747vme

Using ProtoPNet for Interpretable Alzheimer's Disease Classification

Sanaz Mohammadjafari, Mucahit Cevik, Mathusan Thanabalasingam, Ayse Basar, Alzheimer's Disease Neuroimaging Initiative
2021 Proceedings of the Canadian Conference on Artificial Intelligence  
Early detection of Alzheimer's disease (AD) is significant for identifying of better treatment plans for the patients as the AD is not curable.  ...  In this paper, we use ProtoPNet architecture in combination with popular pretrained deep learning models to add interpretability to the AD classifications over MRI scans from ADNI and OASIS datasets.  ...  Acknowledgements 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  ... 
doi:10.21428/594757db.fb59ce6c fatcat:6ppf4lpfcjb6xmssil366mpdsa

Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity

Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, Pattie Maes
2020 Interspeech 2020  
Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space  ...  Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars.  ...  This is consistent with the intuition behind using transfer learning using the trained classification models through the addition of a regression module.  ... 
doi:10.21437/interspeech.2020-3137 dblp:conf/interspeech/SarawgiZSM20 fatcat:pwiuhj35yfapxlz3plftiirbpe

Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity [article]

Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, Pattie Maes
2020 arXiv   pre-print
Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space  ...  Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars.  ...  This is consistent with the intuition behind using transfer learning using the trained classification models through the addition of a regression module.  ... 
arXiv:2009.00700v1 fatcat:a6vcmrtmnzcyfdoawxzdmvemfq

The IEEE Brain Data Bank Challenge 2020

N. N. Chu
2021 IEEE Consumer Electronics Magazine  
Improve deep learning performance using transfer learning to predict early stages of Alzheimer in ADNI dataset, by Team Poke- mon Brain, University of Missouri, Kansas City, MO, USA, which won the first  ...  Utilizing deep learning model to predict brain age for Alzheimer's disease and mild cogni- tive impairment patients, by Team MINE Pro- fessor Brain, National Central University, Taoyuan City, Taiwan.  ... 
doi:10.1109/mce.2021.3051928 fatcat:gsigvkfkcjcxrj336a6cs3mqfq

Classification of Neurodegenerative Disease Stages using Ensemble Machine Learning Classifiers

M. Rohini, D. Surendran
2019 Procedia Computer Science  
The proposed study intends to develop a learning algorithm for the prediction of Alzheimer's disease at an early stage.  ...  The proposed study intends to develop a learning algorithm for the prediction of Alzheimer's disease at an early stage.  ...  The cores of ADNI includes ADNI-1, ADNI-Go, ADNI-2.ADNI also provides pathological and genetic features of disease towards disease progression.  ... 
doi:10.1016/j.procs.2020.01.071 fatcat:u2hpn2beebadvb2upcsqxj4iau

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan  ...  ., +, JBHI Aug. 2020 2420-2429 Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification.  ...  ., +, JBHI May 2020 1379-1393 Relative Afferent Pupillary Defect Screening Through Transfer Learning.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

Design and Implementation of User-specific Information Service Applying Beacon and Internet of Things Technologies at Education Sites

Hyun Joo Kim, Min Sun Kim
2017 International Journal of Advanced Science and Technology  
Alzheimer's disease is the progressive disease that razes memory and other imperative mental functions and is the cause of 60-70% of dementia cases.  ...  In our study, the accuracy of Artificial Neural Network and Deep Learning Neural Networks are compared for normal and abnormal disease diagnosis.  ...  Acknowledgements I express my sincere thanks to Dr D.V.N.Giridhar, Neurologist, Thirupathi for his great support in collection of normal and abnormal MRI images of Alzheimer's disease patients which are  ... 
doi:10.14257/ijast.2017.108.07 fatcat:t4tnrzienzetjdfywasn6f5bgi

Towards Computer-Based Automated Screening of Dementia Through Spontaneous Speech

Karol Chlasta, Krzysztof Wołk
2021 Frontiers in Psychology  
Both methods presented in this paper offer progress toward new, innovative, and more effective computer-based screening of dementia through spontaneous speech.  ...  We discovered that audio transfer learning with a pretrained VGGish feature extractor performs better than the baseline approach using automatically extracted acoustic features.  ...  TABLE 2 | 2 The results of Alzheimer's dementia (AD) classification task on AD Recognition through Spontaneous Speech (ADReSS) test set.  ... 
doi:10.3389/fpsyg.2020.623237 pmid:33643116 pmcid:PMC7907518 fatcat:msp5aw5vyzevhgod7veznmd56u

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

Silvia Basaia, Federica Agosta, Luca Wagner, Elisa Canu, Giuseppe Magnani, Roberto Santangelo, Massimo Filippi
2019 NeuroImage: Clinical  
We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional  ...  High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI  ...  Funding Acknowledgements 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  ... 
doi:10.1016/j.nicl.2018.101645 pmid:30584016 pmcid:PMC6413333 fatcat:vpl6w743vfdwbekpvewny7houe

Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning

Li Kang, Jingwan Jiang, Jianjun Huang, Tijiang Zhang
2020 Frontiers in Aging Neuroscience  
Mild cognitive impairment (MCI) is a clinical state with a high risk of conversion to Alzheimer's Disease (AD).  ...  Then, a convolutional neural network based on transfer learning technique is developed to extract features of the multi-modality data, where an L1-norm is introduced to reduce the feature dimensionality  ...  In Alzheimer's disease, neurons in parts of the brain involved in cognitive function are eventually damaged or destroyed.  ... 
doi:10.3389/fnagi.2020.00206 pmid:33101003 pmcid:PMC7498722 fatcat:33giccsfq5gd7hixkmgjgtkulm
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