MRI Images Analysis Method for Early Stage Alzheimer's Disease Detection

• URL: http://arxiv.org/abs/2012.00830v1
• Date: Fri, 27 Nov 2020 12:36:36 GMT
• Title: MRI Images Analysis Method for Early Stage Alzheimer's Disease Detection
• Authors: Achraf Ben Miled, Taoufik Yeferny, and Amira ben Rabeh
• Abstract summary: Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. We introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from MRI images. The proposed method achieved 96.83% accuracy by using 420 subjects: 210 Normal and 210 MRI.
• Score: 0.28675177318965034
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from Magnetic Resonance Imaging (MRI) images in order to detect the Alzheimer's disease at the MCI stage. The proposed method is evaluated using a big database from OASIS Database Brain. Various sections of the brain: frontal, sagittal and axial were used. The proposed method achieved 96.83% accuracy by using 420 subjects: 210 Normal and 210 MRI

Related papers

• Early diagnosis of Alzheimer's disease from MRI images with deep learning model [0.7673339435080445]
  Alzheimer's disease is the most common cause of dementia worldwide. classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI) In this article, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images.
  arXiv  Detail & Related papers  (2024-09-27T15:07:26Z)
• Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches [0.0]
  Alzheimer's disease weakens several brain processes (such as memory) and eventually results in death. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. We analyzed five specific studies focused on Alzheimer's disease diagnosis using MRI-based deep learning algorithms between 2021 and 2023.
  arXiv  Detail & Related papers  (2023-10-26T19:48:08Z)
• 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)
• Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images [21.514626584695897]
  We propose an end-to-end 3D CNN framework for Alzheimer's disease diagnosis based on ResNet. Our model can focus on key brain regions related to the disease diagnosis. Our method was verified in ablation experiments with two modality images on 792 subjects.
  arXiv  Detail & Related papers  (2023-08-10T15:53:35Z)
• 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)
• Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity [48.75665245214903]
  We propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling.
  arXiv  Detail & Related papers  (2022-09-22T04:17:57Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• 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)
• 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)
• Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data [8.609787905151563]
  Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control: and has achieved 99.89% classification accuracy with imbalanced classes.
  arXiv  Detail & Related papers  (2021-01-08T06:51:08Z)
• 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)

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.