scholarly journals Alzheimer’s Disease: Classification and Detection using MRI Dataset

2021 ◽  
Vol 10 (5) ◽  
pp. 70-72
Author(s):  
V P Suhaira ◽  
Sita S ◽  
Joby George
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Heart Function ◽  
Brain Mri ◽  
Memory Loss ◽  
Disease Classification ◽  
Data Set ◽  
Mri Scans ◽  
Disease Experience ◽  
The Brain

Alzheimer's disease (AD) is a hereditary brain condition that is incurable and progresses over time. Patients with Alzheimer's disease experience memory loss, uncertainty, and difficulty speaking, reading, and writing as a result of this condition. Alzheimer's disease eventually affects the portion of the brain that controls breathing and heart function, leading to death. This framework proposes the OASIS (Open Access Series of Imaging Studies) dataset, which contains the existing MRI data set, which is comprised of a longitudinal sample of 150 subjects aged 60 to 96 who were all acquired on the same scanner using similar sequences. This paper uses a combination of brain MRI scans and psychological parameters to predict disease with high accuracy using various classifier algorithms, and the results can be compared to improve performance.

scholarly journals Early Detection of Alzheimer’s Disease using Convolutional Neural Network Architecture

2021 ◽  
Vol 8 (2) ◽  
pp. 48-57
Author(s):  
Deepthi Kamath ◽  
Misba Firdose Fathima ◽  
Monica K. P ◽  
Kusuma Mohanchandra
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Brain Mri ◽  
Memory Loss ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Mri Scans

Alzheimer's disease is an extremely popular cause of dementia which leads to memory loss, problem-solving and other thinking abilities that are severe enough to interfere with daily life. Detection of Alzheimer’s at a prior stage is crucial as it can prevent significant damage to the patient’s brain. In this paper, a method to detect Alzheimer’s  Disease from Brain MRI images is proposed. The proposed approach extracts shape features and texture of the Hippocampus region from the MRI scans and a Neural Network is used as a Multi-Class Classifier for detection of AD. The proposed approach is implemented and it gives better accuracy as compared to conventional approaches. In this paper, Convolutional Neural Network is the Neural Network approach used for the detection of AD at a prodromal stage.

scholarly journals Alzheimer's Disease Classification Accuracy is Improved by MRI Harmonization based on Attention-Guided Generative Adversarial Networks

2021 ◽  
Author(s):  
surabhi sinha ◽  
Sophia I. Thomopoulos ◽  
Pradeep Lam ◽  
Alexandra Muir ◽  
Paul M. Thompson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Domain Adaptation ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Memory Loss ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network

Alzheimer's disease (AD) accounts for 60% of dementia cases worldwide; patients with the disease typically suffer from irreversible memory loss and progressive decline in multiple cognitive domains. With brain imaging techniques such as magnetic resonance imaging (MRI), microscopic brain changes are detectable even before abnormal memory loss is detected clinically. Patterns of brain atrophy can be measured using MRI, which gives us an opportunity to facilitate AD detection using image classification techniques. Even so, MRI scanning protocols and scanners differ across studies. The resulting differences in image contrast and signal to noise make it important to train and test classification models on multiple datasets, and to handle shifts in image characteristics across protocols (also known as domain transfer or domain adaptation). Here, we examined whether adversarial domain adaptation can boost the performance of a Convolutional Neural Network (CNN) model designed to classify AD. To test this, we used an Attention-Guided Generative Adversarial Network (GAN) to harmonize images from three publicly available brain MRI datasets - ADNI, AIBL and OASIS - adjusting for scanner-dependent effects. Our AG-GAN optimized a joint objective function that included attention loss, pixel loss, cycle-consistency loss and adversarial loss; the model was trained bidirectionally in an end-to-end fashion. For AD classification, we adapted the popular 2D AlexNet CNN to handle 3D images. Classification based on harmonized MR images significantly outperformed classification based on the three datasets in non-harmonized form, motivating further work on image harmonization using adversarial techniques.

scholarly journals Case of early-onset Alzheimer’s disease with atypical manifestation

2021 ◽  
Vol 34 (1) ◽  
pp. e100283
Author(s):  
Lin Zhu ◽  
Limin Sun ◽  
Lin Sun ◽  
Shifu Xiao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Manifestation ◽  
Early Onset ◽  
Short Term Memory ◽  
Brain Mri ◽  
Memory Loss ◽  
Amyloid Β ◽  
Amyloid Β Protein ◽  
Fluent Aphasia

Short-term memory decline is the typical clinical manifestation of Alzheimer’s disease (AD). However, early-onset AD usually has atypical symptoms and may get misdiagnosed. In the present case study, we reported a patient who experienced symptoms of memory loss with progressive non-fluent aphasia accompanied by gradual social withdrawal. He did not meet the diagnostic criteria of AD based on the clinical manifestation and brain MRI. However, his cerebrospinal fluid examination showed a decreased level of beta-amyloid 42, and increased total tau and phosphorylated tau. Massive amyloid β-protein deposition by 11C-Pittsburgh positron emission tomography confirmed the diagnosis of frontal variant AD. This case indicated that early-onset AD may have progressive non-fluent aphasia as the core manifestation. The combination of individual and precision diagnosis would be beneficial for similar cases.

MR Brain Screening using Optimization Techniques – A survey

2021 ◽  
Vol 17 ◽  
Author(s):  
Chitradevi D ◽  
Prabha S.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Symptoms ◽  
Memory Loss ◽  
Amyloid Β ◽  
Cell Loss ◽  
Optimization Techniques ◽  
Amyloid Β Protein ◽  
The Brain ◽  
Brain Tissues

Background: Alzheimer’s disease (AD) is associated with Dementia, and it is also a memory syndrome in the brain. It affects the brain tissues and causes major changes in day-to-day activities. Aging is a major cause of Alzheimer's disease. AD is characterized by two pathological hallmarks as, Amyloid β protein and neurofibrillary tangles of hyperphosphorylated tau protein. The imaging hallmarks for Alzheimer’s disease are namely, swelling, shrinkage of brain tissues due to cell loss, and atrophy in the brain due to protein dissemination. Based on the survey, 60% to 80% of dementia patients belong to Alzheimer’s disease. Introduction: AD is now becoming an increasing and important brain disease. The goal of AD pathology is to cause changes/damage in brain tissues. Alzheimer's disease is thought to begin 20 years or more before symptoms appear, with tiny changes in the brain that are undetectable to the person affected. The changes in a person's brain after a few years are noticeable through symptoms such as language difficulties and memory loss. Neurons in different parts of the brain have detected symptoms such as cognitive impairments and learning disabilities. In this case, neuroimaging tools are necessary to identify the development of pathology which relates to the clinical symptoms. Methods: Several approaches have been tried during the last two decades for brain screening to analyse AD with the process of pre-processing, segmentation and classification. Different individual such as Grey Wolf optimization, Lion Optimization, Ant Lion Optimization and so on. Similarly, hybrid optimization techniques are also attempted to segment the brain sub-regions which helps in identifying the bio-markers to analyse AD. Conclusion: This study discusses a review of neuroimaging technologies for diagnosing Alzheimer's disease, as well as the discovery of hallmarks for the disease and the methodologies for finding hallmarks from brain images to evaluate AD. According to the literature review, most of the techniques predicted higher accuracy (more than 90%), which is beneficial for assessing and screening neurodegenerative illness, particularly Alzheimer's disease.

scholarly journals Low-Dose Ionizing Radiation Modulates Microglia Phenotypes in the Models of Alzheimer’s Disease

2020 ◽  
Vol 21 (12) ◽  
pp. 4532 ◽  
Author(s):  
Sujin Kim ◽  
Hyunju Chung ◽  
Han Ngoc Mai ◽  
Yunkwon Nam ◽  
Soo Jung Shin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ionizing Radiation ◽  
Cognitive Deficits ◽  
Low Dose ◽  
Memory Loss ◽  
M2 Polarization ◽  
Phenotype Switching ◽  
5Xfad Mice ◽  
The Brain

Alzheimer’s disease (AD) is the most common type of dementia. AD involves major pathologies such as amyloid-β (Aβ) plaques and neurofibrillary tangles in the brain. During the progression of AD, microglia can be polarized from anti-inflammatory M2 to pro-inflammatory M1 phenotype. The activation of triggering receptor expressed on myeloid cells 2 (TREM2) may result in microglia phenotype switching from M1 to M2, which finally attenuated Aβ deposition and memory loss in AD. Low-dose ionizing radiation (LDIR) is known to ameliorate Aβ pathology and cognitive deficits in AD; however, the therapeutic mechanisms of LDIR against AD-related pathology have been little studied. First, we reconfirm that LDIR (two Gy per fraction for five times)-treated six-month 5XFAD mice exhibited (1) the reduction of Aβ deposition, as reflected by thioflavins S staining, and (2) the improvement of cognitive deficits, as revealed by Morris water maze test, compared to sham-exposed 5XFAD mice. To elucidate the mechanisms of LDIR-induced inhibition of Aβ accumulation and memory loss in AD, we examined whether LDIR regulates the microglial phenotype through the examination of levels of M1 and M2 cytokines in 5XFAD mice. In addition, we investigated the direct effects of LDIR on lipopolysaccharide (LPS)-induced production and secretion of M1/M2 cytokines in the BV-2 microglial cells. In the LPS- and LDIR-treated BV-2 cells, the M2 phenotypic marker CD206 was significantly increased, compared with LPS- and sham-treated BV-2 cells. Finally, the effect of LDIR on M2 polarization was confirmed by detection of increased expression of TREM2 in LPS-induced BV2 cells. These results suggest that LDIR directly induced phenotype switching from M1 to M2 in the brain with AD. Taken together, our results indicated that LDIR modulates LPS- and Aβ-induced neuroinflammation by promoting M2 polarization via TREM2 expression, and has beneficial effects in the AD-related pathology such as Aβ deposition and memory loss.

scholarly journals IL-33 ameliorates Alzheimer’s disease-like pathology and cognitive decline

2016 ◽  
Vol 113 (19) ◽  
pp. E2705-E2713 ◽  
Author(s):  
Amy K. Y. Fu ◽  
Kwok-Wang Hung ◽  
Michael Y. F. Yuen ◽  
Xiaopu Zhou ◽  
Deejay S. Y. Mak ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Immune Response ◽  
Innate Immune Response ◽  
Memory Loss ◽  
Neuronal Loss ◽  
Innate Immune ◽  
Therapeutic Role ◽  
Potential Therapeutic Role ◽  
The Brain

Alzheimer’s disease (AD) is a devastating condition with no known effective treatment. AD is characterized by memory loss as well as impaired locomotor ability, reasoning, and judgment. Emerging evidence suggests that the innate immune response plays a major role in the pathogenesis of AD. In AD, the accumulation of β-amyloid (Aβ) in the brain perturbs physiological functions of the brain, including synaptic and neuronal dysfunction, microglial activation, and neuronal loss. Serum levels of soluble ST2 (sST2), a decoy receptor for interleukin (IL)-33, increase in patients with mild cognitive impairment, suggesting that impaired IL-33/ST2 signaling may contribute to the pathogenesis of AD. Therefore, we investigated the potential therapeutic role of IL-33 in AD, using transgenic mouse models. Here we report that IL-33 administration reverses synaptic plasticity impairment and memory deficits in APP/PS1 mice. IL-33 administration reduces soluble Aβ levels and amyloid plaque deposition by promoting the recruitment and Aβ phagocytic activity of microglia; this is mediated by ST2/p38 signaling activation. Furthermore, IL-33 injection modulates the innate immune response by polarizing microglia/macrophages toward an antiinflammatory phenotype and reducing the expression of proinflammatory genes, including IL-1β, IL-6, and NLRP3, in the cortices of APP/PS1 mice. Collectively, our results demonstrate a potential therapeutic role for IL-33 in AD.

scholarly journals Alzheimer’s disease under the purview of Graph Theory Centric Genetic Networks

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Yegnanarayanan Venkatraman ◽  
◽  
Narayanaa Y Krithicaa ◽  
Valentina E. Balas ◽  
Marius M. Balas ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Network Theory ◽  
Memory Loss ◽  
Amyloid Plaque ◽  
Brain Regions ◽  
Protein Protein Interaction ◽  
Communication Demands ◽  
The Brain

Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.

scholarly journals Image processing for early detection of Alzheimer’s disease using Iridology

2020 ◽  
Vol 11 (4) ◽  
pp. 5555-5559
Author(s):  
Asuntha A ◽  
Sai Kalyan Reddy R ◽  
Vamshikrishna K ◽  
Premsagar N
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Region Of Interest ◽  
Colour Pattern ◽  
Training Methods ◽  
Iris Image ◽  
Alternative Research ◽  
The Brain

Alzheimer's disease is caused by genetics, personal lifestyle and other environmental factors. It is an irreversible disease that slowly destroys the brain memory cells. There are no specific methods for the detection of Alzheimer's disease. The primary symptoms of Alzheimer's disease are memory loss, difficulty in thinking, a problem in writing and speaking and others. Iridology is alternative research that has gained more popularity in recent years, which studies the alterations of the iris in correspondence with the organs of the human body. The combination of digital image processing with Iridology gives an excellent opportunity to explore and learn about different neuronal diseases, specifically Alzheimer's disease. In this work, MATLAB software is applied to determine the colour, pattern and other factors that show the existence of Alzheimer's disease. The noise in the iris image is removed by the Gaussian filter, followed by histogram analyses and cropping. The Hough circle transform is used to identify the region of interest and to convert the circular iris image into rectangle form. In the training methods, the SVM and CNN classifiers are used to classify whether the person has Alzheimer's disease. Finally, the results are compared with the real-time images.

scholarly journals Early Recognition of Alzheimer’s Disease using Brain MRI

2017 ◽  
Vol 2 (1) ◽  
pp. 6
Author(s):  
Rania Ahmed Kadry Abdel Gawad Birry
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Euclidean Distance ◽  
Early Recognition ◽  
Brain Mri ◽  
Extraction Techniques ◽  
City Block ◽  
Cosine Distance ◽  
City Block Distance ◽  
The Brain

Abstract—Alzheimer’s disease (AD) is a brain disease that causes a slow decline in memory, thinking and reasoning skills. It represents a major public health problem.  Magnetic Resonance Imaging (MRI) have shown that the brains of people with (AD) shrink significantly as the disease progresses. This shrinkage appears in specific brain regions such as the hippocampus which is a small, curved formation in the brain that plays an important role in the limbic system also involved in the formation of new memories and is also associated with learning and emotions.  Medical information on brain MRI is used in detecting the abnormalities in physiological structures. Structural MRI measurements can detect and follow the evolution of brain atrophy which is a marker of the disease progression; therefore, it allows diagnosis and prediction of AD.  The research’s main target is the early recognition of Alzheimer’s disease automatically, which will thereby avoid deterioration of the case resulting in complete brain damage stage.  Alzheimer’s disease yields visible changes in the brain structures. The aim is to recognize if the patient belongs to Alzheimer’s disease category or a normal healthy person at an early stage. Initially, image pre-processing and features extraction techniques are applied including data reduction using Discrete Cosine Transform (DCT) and Cropping, then traditional classification techniques like Euclidean Distance, Chebyshev Distance, Cosine Distance, City Block Distance, and Black pixel counter, were applied on the resulting vectors for classification. Image pre-processing includes noise reduction, Gray-scale conversion and binary scale conversion were applied for the MRI images. Feature extraction techniques follow including cropping and low spatial frequency components (DCT). This paper aims to automatically recognize and detect Alzheimer’s infected brain using MRI, without the need of clinical expert. This early recognition would be helpful to postpone the disease progression and maintain it at an almost steady stage. It was concluded after collecting a dataset of 50 MRI , 25 for normal MRI and  25 for AD MRI that Chebyshev Distance classifier yielded the highest success rate in the recognition of Alzheimer’s disease with accuracy 94% compared to other classification techniques used where, Euclidean Distance is 91.6%,  Cosine Distance is 86.8%, City block Distance is 89.6%, Correlation Distance is 86.4% and Black pixels counter is 90%.

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