192 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 by Deep Learning Using MRI-Based Approaches
The most frequent kind of dementia of the nervous system, Alzheimer's
disease, weakens several brain processes (such as memory) and eventually
results in death. The clinical study uses magnetic resonance imaging to
diagnose AD. Deep learning algorithms are capable of pattern recognition and
feature extraction from the inputted raw data. As early diagnosis and stage
detection are the most crucial elements in enhancing patient care and treatment
outcomes, deep learning algorithms for MRI images have recently allowed for
diagnosing a medical condition at the beginning stage and identifying
particular symptoms of Alzheimer's disease. As a result, we aimed to analyze
five specific studies focused on AD diagnosis using MRI-based deep learning
algorithms between 2021 and 2023 in this study. To completely illustrate the
differences between these techniques and comprehend how deep learning
algorithms function, we attempted to explore selected approaches in depth
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
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
DETERMINING EFFECTIVE LEVEL OF DEMENTIA DISEASE USING MRI IMAGES
Abstract
The prevalence of dementia is growing as the world's population ages, making it a major public health issue. The key to successful management and treatment of dementia is an early and precise diagnosis. In this work, we will investigate the Dementia detection model DenseNet-169 in depth. The DenseNet-169 model has been used to classify almost 7,000 magnetic resonance imaging (MRI) scans of the brain. Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia are all categorized using this Convolution Neural Network (CNN) model. The use of deep learning and image processing presents intriguing new directions for the diagnosis and treatment of dementia, with the ultimate goal of enhancing the quality of life for those with the disease
Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective
Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinician
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
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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