scholarly journals Detecting biological changes in longitudinal MRI scans of Alzheimer’s disease patients in the hippocampus region with deep learning

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Mengjin Dong ◽  
Long Xie ◽  
Sandhitsu R. Das ◽  
David A. Wolk ◽  
Paul A. Yushkevich
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Longitudinal Mri ◽  
Mri Scans

scholarly journals Classification of Alzheimer’s Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Monika Sethi ◽  
Sachin Ahuja ◽  
Shalli Rani ◽  
Puneet Bawa ◽  
Atef Zaguia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Bayesian Optimization ◽  
Successful Implementation ◽  
Search Optimization ◽  
Mri Scans ◽  
Deep Learning Model

Alzheimer’s disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

scholarly journals Sensitive biomarkers to Alzheimer's disease progression in longitudinal MRI from deep learning

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Mengjin Dong ◽  
Long Xie ◽  
Jiancong Wang ◽  
Sandhitsu R Das ◽  
David A Wolk ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Disease Progression ◽  
Longitudinal Mri

scholarly journals Risk prediction of prodromal Alzheimer’s disease using deep learning of MRI scans

2021 ◽  
Vol 17 (S1) ◽  
Author(s):  
Shaikh Basha ◽  
Moira Marizzoni ◽  
Giovanni B Frisoni ◽  
Jorge Jovicich ◽  
Krishna P Miyapuram
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Risk Prediction ◽  
Mri Scans ◽  
Prodromal Alzheimer’S Disease

scholarly journals Multi-View Based Multi-Model Learning for MCI Diagnosis

2020 ◽  
Vol 10 (3) ◽  
pp. 181
Author(s):  
Ping Cao ◽  
Jie Gao ◽  
Zuping Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Local Information ◽  
Global Information ◽  
Model Learning ◽  
3D Images ◽  
Learning Framework ◽  
Mri Scans

Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).

Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease

2020 ◽  
pp. 1358-1382
Author(s):  
Rekh Ram Janghel
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Magnetic Resonance Image ◽  
Cross Validation ◽  
Convolution Neural Network ◽  
Learning Networks ◽  
Mri Scans

Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet, and R-CNN architecture. The best accuracies for 75-25 cross validation and 90-10 cross validation are 97.68% and 98.75%, respectively, and achieved by ZFNet architecture of convolution neural network. This research will help in further studies on improving the accuracy of Alzheimer's diagnosis and prediction using neural networks.

Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease

2018 ◽  
pp. 193-217
Author(s):  
Rekh Ram Janghel
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Magnetic Resonance Image ◽  
Cross Validation ◽  
Convolution Neural Network ◽  
Learning Networks ◽  
Mri Scans

Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet, and R-CNN architecture. The best accuracies for 75-25 cross validation and 90-10 cross validation are 97.68% and 98.75%, respectively, and achieved by ZFNet architecture of convolution neural network. This research will help in further studies on improving the accuracy of Alzheimer's diagnosis and prediction using neural networks.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

Sign in / Sign up

Export Citation Format

Share Document