404 research outputs found
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
Towards Practical Application of Deep Learning in Diagnosis of Alzheimer's Disease
Accurate diagnosis of Alzheimer's disease (AD) is both challenging and time
consuming. With a systematic approach for early detection and diagnosis of AD,
steps can be taken towards the treatment and prevention of the disease. This
study explores the practical application of deep learning models for diagnosis
of AD. Due to computational complexity, large training times and limited
availability of labelled dataset, a 3D full brain CNN (convolutional neural
network) is not commonly used, and researchers often prefer 2D CNN variants. In
this study, full brain 3D version of well-known 2D CNNs were designed, trained
and tested for diagnosis of various stages of AD. Deep learning approach shows
good performance in differentiating various stages of AD for more than 1500
full brain volumes. Along with classification, the deep learning model is
capable of extracting features which are key in differentiating the various
categories. The extracted features align with meaningful anatomical landmarks,
that are currently considered important in identification of AD by experts. An
ensemble of all the algorithm was also tested and the performance of the
ensemble algorithm was superior to any individual algorithm, further improving
diagnosis ability. The 3D versions of the trained CNNs and their ensemble have
the potential to be incorporated in software packages that can be used by
physicians/radiologists to assist them in better diagnosis of AD.Comment: 18 pages, 8 figure
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
Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review
Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions
Alzheimer’s Disease Diagnosis Using CNN Based Pre-trained Models
Memory loss and impairment are signs of Alzheimer's disease (AD), which may also cause other issues. It has a significant impact on patients' lives and is incurable, but rapid recognition of Alzheimer's disease can be useful to initiate appropriate therapy to avoid further deterioration to the brain. Previously, Machine Learning methodswere used to detect Alzheimer's disease. In recent times, Deep Learning algorithms have become more popular for pattern recognition. This workconcentrates on the recognition of Alzheimer's disease at a preliminary phase using advanced convolutional neural network models. As the disease advances, they steadily forget everything. It is critical to detect the disease as quickly as possible. The proposed model usespre-trained models that uses magnetic resonance imaging of the brain to determine if a person has very mild, mild, moderate, or non-dementia. The models used for classification are VGG16, VGG19, and ResNet50 architectures and provide performance comparison
A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease
Alzheimer's disease (AD) is the most common long-term illness in elderly
people. In recent years, deep learning has become popular in the area of
medical imaging and has had a lot of success there. It has become the most
effective way to look at medical images. When it comes to detecting AD, the
deep neural model is more accurate and effective than general machine learning.
Our research contributes to the development of a more comprehensive
understanding and detection of the disease by identifying four distinct classes
that are predictive of AD with a high weighted accuracy of 98.91%. A unique
strategy has been proposed to improve the accuracy of the imbalance dataset
classification problem via the combination of ensemble averaging models and
five different transfer learning models in this study.
EfficientNetB0+Resnet152(effnet+res152) and
InceptionV3+EfficientNetB0+Resnet50(incep+effnet+res50) models have been
fine-tuned and have reached the highest weighted accuracy for multi-class AD
stage classifications
Alzheimer's Disease: A Survey
Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease
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