199 research outputs found
Alzheimers Disease Diagnosis using Machine Learning: A Review
Alzheimers Disease AD is an acute neuro disease that degenerates the brain
cells and thus leads to memory loss progressively. It is a fatal brain disease
that mostly affects the elderly. It steers the decline of cognitive and
biological functions of the brain and shrinks the brain successively, which in
turn is known as Atrophy. For an accurate diagnosis of Alzheimers disease,
cutting edge methods like machine learning are essential. Recently, machine
learning has gained a lot of attention and popularity in the medical industry.
As the illness progresses, those with Alzheimers have a far more difficult time
doing even the most basic tasks, and in the worst case, their brain completely
stops functioning. A persons likelihood of having early-stage Alzheimers
disease may be determined using the ML method. In this analysis, papers on
Alzheimers disease diagnosis based on deep learning techniques and
reinforcement learning between 2008 and 2023 found in google scholar were
studied. Sixty relevant papers obtained after the search was considered for
this study. These papers were analysed based on the biomarkers of AD and the
machine-learning techniques used. The analysis shows that deep learning methods
have an immense ability to extract features and classify AD with good accuracy.
The DRL methods have not been used much in the field of image processing. The
comparison results of deep learning and reinforcement learning illustrate that
the scope of Deep Reinforcement Learning DRL in dementia detection needs to be
explored.Comment: 10 pages and 3 figure
Machine Learning for Multiclass Classification and Prediction of Alzheimer\u27s Disease
Alzheimer\u27s disease (AD) is an irreversible neurodegenerative disorder and a common form of dementia. This research aims to develop machine learning algorithms that diagnose and predict the progression of AD from multimodal heterogonous biomarkers with a focus placed on the early diagnosis. To meet this goal, several machine learning-based methods with their unique characteristics for feature extraction and automated classification, prediction, and visualization have been developed to discern subtle progression trends and predict the trajectory of disease progression.
The methodology envisioned aims to enhance both the multiclass classification accuracy and prediction outcomes by effectively modeling the interplay between the multimodal biomarkers, handle the missing data challenge, and adequately extract all the relevant features that will be fed into the machine learning framework, all in order to understand the subtle changes that happen in the different stages of the disease. This research will also investigate the notion of multitasking to discover how the two processes of multiclass classification and prediction relate to one another in terms of the features they share and whether they could learn from one another for optimizing multiclass classification and prediction accuracy.
This research work also delves into predicting cognitive scores of specific tests over time, using multimodal longitudinal data. The intent is to augment our prospects for analyzing the interplay between the different multimodal features used in the input space to the predicted cognitive scores. Moreover, the power of modality fusion, kernelization, and tensorization have also been investigated to efficiently extract important features hidden in the lower-dimensional feature space without being distracted by those deemed as irrelevant.
With the adage that a picture is worth a thousand words, this dissertation introduces a unique color-coded visualization system with a fully integrated machine learning model for the enhanced diagnosis and prognosis of Alzheimer\u27s disease. The incentive here is to show that through visualization, the challenges imposed by both the variability and interrelatedness of the multimodal features could be overcome. Ultimately, this form of visualization via machine learning informs on the challenges faced with multiclass classification and adds insight into the decision-making process for a diagnosis and prognosis
Robust training of recurrent neural networks to handle missing data for disease progression modeling
Disease progression modeling (DPM) using longitudinal data is a challenging
task in machine learning for healthcare that can provide clinicians with better
tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect
temporal dependencies among measurements and make parametric assumptions about
biomarker trajectories. In addition, they do not model multiple biomarkers
jointly and need to align subjects' trajectories. In this paper, recurrent
neural networks (RNNs) are utilized to address these issues. However, in many
cases, longitudinal cohorts contain incomplete data, which hinders the
application of standard RNNs and requires a pre-processing step such as
imputation of the missing values. We, therefore, propose a generalized training
rule for the most widely used RNN architecture, long short-term memory (LSTM)
networks, that can handle missing values in both target and predictor
variables. This algorithm is applied for modeling the progression of
Alzheimer's disease (AD) using magnetic resonance imaging (MRI) biomarkers. The
results show that the proposed LSTM algorithm achieves a lower mean absolute
error for prediction of measurements across all considered MRI biomarkers
compared to using standard LSTM networks with data imputation or using a
regression-based DPM method. Moreover, applying linear discriminant analysis to
the biomarkers' values predicted by the proposed algorithm results in a larger
area under the receiver operating characteristic curve (AUC) for clinical
diagnosis of AD compared to the same alternatives, and the AUC is comparable to
state-of-the-art AUCs from a recent cross-sectional medical image
classification challenge. This paper shows that built-in handling of missing
values in LSTM network training paves the way for application of RNNs in
disease progression modeling.Comment: 9 pages, 1 figure, MIDL conferenc
Automated detection of Alzheimer disease using MRI images and deep neural networks- A review
Early detection of Alzheimer disease is crucial for deploying interventions
and slowing the disease progression. A lot of machine learning and deep
learning algorithms have been explored in the past decade with the aim of
building an automated detection for Alzheimer. Advancements in data
augmentation techniques and advanced deep learning architectures have opened up
new frontiers in this field, and research is moving at a rapid speed. Hence,
the purpose of this survey is to provide an overview of recent research on deep
learning models for Alzheimer disease diagnosis. In addition to categorizing
the numerous data sources, neural network architectures, and commonly used
assessment measures, we also classify implementation and reproducibility. Our
objective is to assist interested researchers in keeping up with the newest
developments and in reproducing earlier investigations as benchmarks. In
addition, we also indicate future research directions for this topic.Comment: 22 Pages, 5 Figures, 7 Table
Predicting Alzheimer’s disease progression using multi-modal deep learning approach
Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials
Alzheimer Disease Detection Techniques and Methods: A Review
Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper
Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease
Background: Although convolutional neural networks (CNN) achieve high
diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on
magnetic resonance imaging (MRI) scans, they are not yet applied in clinical
routine. One important reason for this is a lack of model comprehensibility.
Recently developed visualization methods for deriving CNN relevance maps may
help to fill this gap. We investigated whether models with higher accuracy also
rely more on discriminative brain regions predefined by prior knowledge.
Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI
scans of patients with dementia and amnestic mild cognitive impairment (MCI)
and verified the accuracy of the models via cross-validation and in three
independent samples including N=1655 cases. We evaluated the association of
relevance scores and hippocampus volume to validate the clinical utility of
this approach. To improve model comprehensibility, we implemented an
interactive visualization of 3D CNN relevance maps.
Results: Across three independent datasets, group separation showed high
accuracy for AD dementia vs. controls (AUC0.92) and moderate accuracy for
MCI vs. controls (AUC0.75). Relevance maps indicated that hippocampal
atrophy was considered as the most informative factor for AD detection, with
additional contributions from atrophy in other cortical and subcortical
regions. Relevance scores within the hippocampus were highly correlated with
hippocampal volumes (Pearson's r-0.86, p<0.001).
Conclusion: The relevance maps highlighted atrophy in regions that we had
hypothesized a priori. This strengthens the comprehensibility of the CNN
models, which were trained in a purely data-driven manner based on the scans
and diagnosis labels.Comment: 24 pages, 9 figures/tables, supplementary material, source code
available on GitHu
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