Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods

• URL: http://arxiv.org/abs/2501.15293v2
• Date: Wed, 29 Jan 2025 10:30:35 GMT
• Title: Deep Learning in Early Alzheimer's disease's Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods
• Authors: Rubab Hafeez, Sadia Waheed, Syeda Aleena Naqvi, Fahad Maqbool, Amna Sarwar, Sajjad Saleem, Muhammad Imran Sharif, Kamran Siddique, Zahid Akhtar,
• Abstract summary: 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.
• Score: 4.072815076028185
• License:
• Abstract: Alzheimers disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimers disease promotes brain shrinkage, ultimately leading to dementia. Dementia diagnosis typically takes 2.8 to 4.4 years after the first clinical indication. Advancements in computing and information technology have led to many techniques of studying Alzheimers disease. Early identification and therapy are crucial for preventing Alzheimers disease, as early-onset dementia hits people before the age of 65, while late-onset dementia occurs after this age. According to the 2015 World Alzheimers disease Report, there are 46.8 million individuals worldwide suffering from dementia, with an anticipated 74.7 million more by 2030 and 131.5 million by 2050. Deep Learning has outperformed conventional Machine Learning techniques by identifying intricate structures in high-dimensional data. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have achieved an accuracy of up to 96.0% for Alzheimers disease classification, and 84.2% for mild cognitive impairment (MCI) conversion prediction. There have been few literature surveys available on applying ML to predict dementia, lacking in congenital observations. However, this survey has focused on a specific data channel for dementia detection. This study evaluated Deep Learning algorithms for early Alzheimers disease detection, using openly accessible datasets, feature segmentation, and classification methods. This article also has identified research gaps and limits in detecting Alzheimers disease, which can inform future research.

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)
• Introducing an ensemble method for the early detection of Alzheimer's disease through the analysis of PET scan images [0.8192907805418583]
  This study delves into the challenging task of classifying Alzheimer's disease into four distinct groups: control normal (CN), progressive mild cognitive impairment (pMCI), stable mild cognitive impairment (sMCI), and Alzheimer's disease (AD) Several deep-learning and traditional machine-learning models have been used to detect Alzheimer's disease. The results show that using deep-learning models to tell the difference between MCI patients gives an overall average accuracy of 93.13% and an AUC of 94.4%.
  arXiv  Detail & Related papers  (2024-03-17T16:12:50Z)
• Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans [8.684668542584701]
  15 million Americans will have either clinical AD or mild cognitive impairment by 2060. We aim to predict the stage of the disease based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.
  arXiv  Detail & Related papers  (2023-11-30T04:32:28Z)
• Alzheimers Disease Diagnosis using Machine Learning: A Review [0.0]
  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.
  arXiv  Detail & Related papers  (2023-04-17T17:50:22Z)
• Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks [53.257804915263165]
  Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. Key evidence from neuroimaging data for pathological commonness remains unrevealed. We build a deep learning model, using multi-site functional magnetic resonance imaging data, for classifying 5 different brain disorders from healthy controls.
  arXiv  Detail & Related papers  (2023-02-23T09:22:05Z)
• Investigating Conversion from Mild Cognitive Impairment to Alzheimer's Disease using Latent Space Manipulation [0.23931689873603598]
  We propose a deep learning framework to discover the variables which are identifiers of the conversion from MCI to Alzheimer's disease. In particular, the latent space of a variational auto-encoder network trained with the MCI and Alzheimer's patients is manipulated to obtain the significant attributes. By utilizing a generative decoder and the dimensions that lead to the Alzheimer's diagnosis, we generate synthetic dementia patients from MCI patients in the dataset.
  arXiv  Detail & Related papers  (2021-11-16T21:48:09Z)
• 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)
• Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity [39.57255380551913]
  We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect Alzheimer's dementia (AD) and its severity. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression.
  arXiv  Detail & Related papers  (2020-08-30T21:47:26Z)
• 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)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

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.