Alzheimers Disease Diagnosis using Machine Learning: A Review

• URL: http://arxiv.org/abs/2304.09178v1
• Date: Mon, 17 Apr 2023 17:50:22 GMT
• Title: Alzheimers Disease Diagnosis using Machine Learning: A Review
• Authors: Nair Bini Balakrishnan, P.S. Sreeja, Jisha Jose Panackal
• Abstract summary: Alzheimers Disease AD is an acute neuro disease that degenerates the brain cells and thus leads to memory loss progressively. For an accurate diagnosis of Alzheimers disease, cutting edge methods like machine learning are essential.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models [0.0]
  Alzheimer's disease is an untreatable, progressive brain disorder that slowly robs people of their memory, thinking abilities, and capacity to complete even the most basic tasks. This study proposes a computer aided diagnosis system to detect Alzheimer's disease from biomarkers captured using neuroimaging techniques.
  arXiv  Detail & Related papers  (2025-02-09T08:43:08Z)
• Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods [4.072815076028185]
  According to the 2015 World Alzheimers disease Report, there are 46.8 million individuals worldwide suffering from dementia. Deep Learning has outperformed conventional Machine Learning techniques by identifying intricate structures in high-dimensional data. This study evaluated Deep Learning algorithms for early Alzheimers disease detection, using openly accessible datasets.
  arXiv  Detail & Related papers  (2025-01-25T18:00:17Z)
• Quantum AI for Alzheimer's disease early screening [1.2891210250935148]
  Alzheimer's disease is a neurodegenerative brain disorder that mostly affects elderly people. The DARWIN dataset contains handwriting samples from people affected by Alzheimer's disease and a group of healthy people. We use this dataset to test classical methods for classification and compare their performances with the ones obtained via quantum machine learning methods.
  arXiv  Detail & Related papers  (2024-05-01T07:55:08Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline [54.93591298333767]
  Brain diffuser is a diffusion based end-to-end brain network generative model. It exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects. For the case of Alzheimer's disease, the proposed model performs better than the results from existing toolkits on the Alzheimer's Disease Neuroimaging Initiative database.
  arXiv  Detail & Related papers  (2023-03-11T14:04:58Z)
• A Convolutional-based Model for Early Prediction of Alzheimer's based on the Dementia Stage in the MRI Brain Images [0.0]
  Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images.
  arXiv  Detail & Related papers  (2023-02-02T21:10:31Z)
• Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction [53.122045119395594]
  Unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results. We propose deep learning for UAD in 3D brain MRI considering additional age information. Based on our analysis, we propose a novel deep learning approach for UAD with multi-task age prediction.
  arXiv  Detail & Related papers  (2022-01-31T09:39:52Z)
• Predicting Alzheimer's Disease Using 3DMgNet [2.97983501982132]
  3DMgNet is a unified framework of multigrid and convolutional neural network to diagnose Alzheimer's disease (AD) The model achieved 92.133% accuracy for AD vs NC classification and significantly reduced the model parameters.
  arXiv  Detail & Related papers  (2022-01-12T09:08:08Z)
• Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss [75.03117866578913]
  A novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Experiments with $6586$ MRIs showed that TSAN could provide accurate brain age estimation.
  arXiv  Detail & Related papers  (2021-06-06T07:11:25Z)
• Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques [111.165389441988]
  This work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI) Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
  arXiv  Detail & Related papers  (2021-05-18T11:37:57Z)
• Patch-based Brain Age Estimation from MR Images [64.66978138243083]
  Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age. Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals. We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
  arXiv  Detail & Related papers  (2020-08-29T11:50:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.