Early diagnosis of Alzheimer's disease from MRI images with deep learning model

• URL: http://arxiv.org/abs/2409.18814v1
• Date: Fri, 27 Sep 2024 15:07:26 GMT
• Title: Early diagnosis of Alzheimer's disease from MRI images with deep learning model
• Authors: Sajjad Aghasi Javid, Mahmood Mohassel Feghhi,
• Abstract summary: 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.
• Score: 0.7673339435080445
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: It is acknowledged that the most common cause of dementia worldwide is Alzheimer's disease (AD). This condition progresses in severity from mild to severe and interferes with people's everyday routines. Early diagnosis plays a critical role in patient care and clinical trials. Convolutional neural networks (CNN) are used to create a framework for identifying specific disease features from MRI scans Classification of dementia involves approaches such as medical history review, neuropsychological tests, and magnetic resonance imaging (MRI). However, the image dataset obtained from Kaggle faces a significant issue of class imbalance, which requires equal distribution of samples from each class to address. In this article, to address this imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is utilized. Furthermore, a pre-trained convolutional neural network has been applied to the DEMNET dementia network to extract key features from AD images. The proposed model achieved an impressive accuracy of 98.67%.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• A Dual-Attention Aware Deep Convolutional Neural Network for Early Alzheimer's Detection [0.0]
  Alzheimer's disease (AD) represents the primary form of neurodegeneration, impacting millions of individuals each year. We introduce a dual attention enhanced deep learning framework for classifying AD from neuroimaging data.
  arXiv  Detail & Related papers  (2024-07-15T17:22:16Z)
• A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
  We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics. We generate high-quality synthetic fMRI data based on user-supplied demographics.
  arXiv  Detail & Related papers  (2024-05-13T17:49:20Z)
• Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease [2.6793044027881865]
  This study proposes a novel Alzheimer's disease detection model based on Convolutional Neural Networks. The experimental results indicate that the employed 2D fusion algorithm effectively improves the model's training expense.
  arXiv  Detail & Related papers  (2024-01-25T12:18:46Z)
• 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)
• 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)
• 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)
• A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images [0.23633885460047763]
  We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images.
  arXiv  Detail & Related papers  (2022-03-03T15:14:13Z)
• 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.