Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models

• URL: http://arxiv.org/abs/2502.05815v1
• Date: Sun, 09 Feb 2025 08:43:08 GMT
• Title: Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models
• Authors: Nasser A Alsadhan,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: Alzheimer's disease is an untreatable, progressive brain disorder that slowly robs people of their memory, thinking abilities, and ultimately their capacity to complete even the most basic tasks. Among older adults, it is the most frequent cause of dementia. Although there is presently no treatment for Alzheimer's disease, scientific trials are ongoing to discover drugs to combat the condition. Treatments to slow the signs of dementia are also available. Many researchers throughout the world became interested in developing computer-aided diagnosis systems to aid in the early identification of this deadly disease and assure an accurate diagnosis. In particular, image based approaches have been coupled with machine learning techniques to address the challenges of Alzheimer's disease detection. This study proposes a computer aided diagnosis system to detect Alzheimer's disease from biomarkers captured using neuroimaging techniques. The proposed approach relies on deep learning techniques to extract the relevant visual features from the image collection to accurately predict the Alzheimer's class value. In the experiments, standard datasets and pre-trained deep learning models were investigated. Moreover, standard performance measures were used to assess the models' performances. The obtained results proved that VGG16-based models outperform the state of the art performance.

Related papers

• 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)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• 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)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Detection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine Learning [0.0]
  Alzheimer's Disease is a devastating neurological disorder that is increasingly affecting the elderly population. We present a novel approach for detecting four different stages of Alzheimer's disease from MRI scan images based on inertia tensor analysis and machine learning.
  arXiv  Detail & Related papers  (2023-04-26T06:37:14Z)
• 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)
• Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease [0.0]
  This paper proposes a transfer learning method using class decomposition to detect Alzheimer's disease from sMRI images. The proposed model achieved state-of-the-art performance in the Alzheimer's disease (AD) vs mild cognitive impairment (MCI) vs cognitively normal (CN) classification task with a 3% increase in accuracy from what is reported in the literature.
  arXiv  Detail & Related papers  (2023-01-31T09:44:52Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans [10.120835953459247]
  Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training.
  arXiv  Detail & Related papers  (2020-11-28T14:25:30Z)
• 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.