471 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
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
Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering
Alzheimer's disease is a kind of degenerative dementia that causes progressively worsening memory loss and other cognitive and physical impairments over time. Mini-Mental State Examinations and other screening tools are helpful for early detection, but diagnostic MRI brain analysis is required. When Alzheimer's disease (AD) is detected in its earliest stages, patients may begin protective treatments before permanent brain damage has occurred. The characteristics of the lesion sites in AD affected role, as identified by MRI, exhibit great variety and are dispersed across the image space, as demonstrated in cross-sectional imaging investigations of the disease. Optimized Adaptive Bilateral filtering using a deep learning model was suggested as part of this study's approach toward this end. Denoising the pictures with the help of the suggested adaptive bilateral filter is the first stage (ABF). The ABF improves denoising in edge, detail, and homogenous areas separately. After then, the ABF is given a weight, and the Adaptive Equilibrium Optimizer is used to determine the best possible value for that weight (AEO). LeNet, a CNN model, is then used to complete the AD organization. The first step in using the LeNet-5 network model to identify AD is to study the model's structure and parameters. The ADNI experimental dataset was used to verify the suggested technique and compare it to other models. The experimental findings prove that the suggested method can achieve a classification accuracy of 97.43%, 98.09% specificity, 97.12% sensitivity, and 89.67% Kappa index. When compared against competing algorithms, the suggested model emerges victorious
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
Novel Deep Learning Models for Medical Imaging Analysis
abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201
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
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