Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI

• URL: http://arxiv.org/abs/2501.15733v1
• Date: Mon, 27 Jan 2025 02:18:08 GMT
• Title: Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI
• Authors: Taymaz Akan, Sait Alp, Md. Shenuarin Bhuiyan, Elizabeth A. Disbrow, Steven A. Conrad, John A. Vanchiere, Christopher G. Kevil, Mohammad A. N. Bhuiyan,
• Abstract summary: Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide. We present "ViTranZheimer," an AD diagnosis approach which leverages video vision transformers to analyze 3D brain MRI data.
• Score: 0.0
• License:
• Abstract: Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide, necessitating early and accurate diagnosis for optimal patient management. In recent years, advancements in deep learning have shown remarkable potential in medical image analysis. Methods In this study, we present "ViTranZheimer," an AD diagnosis approach which leverages video vision transformers to analyze 3D brain MRI data. By treating the 3D MRI volumes as videos, we exploit the temporal dependencies between slices to capture intricate structural relationships. The video vision transformer's self-attention mechanisms enable the model to learn long-range dependencies and identify subtle patterns that may indicate AD progression. Our proposed deep learning framework seeks to enhance the accuracy and sensitivity of AD diagnosis, empowering clinicians with a tool for early detection and intervention. We validate the performance of the video vision transformer using the ADNI dataset and conduct comparative analyses with other relevant models. Results The proposed ViTranZheimer model is compared with two hybrid models, CNN-BiLSTM and ViT-BiLSTM. CNN-BiLSTM is the combination of a convolutional neural network (CNN) and a bidirectional long-short-term memory network (BiLSTM), while ViT-BiLSTM is the combination of a vision transformer (ViT) with BiLSTM. The accuracy levels achieved in the ViTranZheimer, CNN-BiLSTM, and ViT-BiLSTM models are 98.6%, 96.479%, and 97.465%, respectively. ViTranZheimer demonstrated the highest accuracy at 98.6%, outperforming other models in this evaluation metric, indicating its superior performance in this specific evaluation metric. Conclusion This research advances the understanding of applying deep learning techniques in neuroimaging and Alzheimer's disease research, paving the way for earlier and less invasive clinical diagnosis.

Related papers

• ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models [46.80682547774335]
  We propose ScaleMAI, an agent of AI-integrated data curation and annotation. First, ScaleMAI creates a dataset of 25,362 CT scans, including per-voxel annotations for benign/malignant tumors and 24 anatomical structures. Second, through progressive human-in-the-loop iterations, ScaleMAI provides Flagship AI Model that can approach the proficiency of expert annotators in detecting pancreatic tumors.
  arXiv  Detail & Related papers  (2025-01-06T22:12:00Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers [13.743241062824548]
  Alzheimer's disease (AD) presents a formidable global health challenge. Traditional analysis methods often struggle to discern intricate 3D patterns crucial for AD identification. We introduce the 3D Hybrid Compact Convolutional Transformers 3D (HCCT)
  arXiv  Detail & Related papers  (2024-03-24T14:35:06Z)
• Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans [2.621434923709917]
  This study aims to present a hybrid model that combines a CNN model's feature extraction capabilities with an LSTM model's detection capabilities. The training of the hybrid model involved utilizing the ADNI dataset. The model achieved a level of accuracy of 98.8%, a sensitivity rate of 100%, and a specificity rate of 76%.
  arXiv  Detail & Related papers  (2024-03-08T14:34:32Z)
• Multiple Instance Learning for Glioma Diagnosis using Hematoxylin and Eosin Whole Slide Images: An Indian Cohort Study [31.789472128764036]
  This study advances patient care with findings from rigorous multiple instance learning experimentations. It establishes new performance benchmarks in glioma subtype classification across multiple datasets.
  arXiv  Detail & Related papers  (2024-02-24T14:59:19Z)
• Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs [0.7770029179741429]
  This paper introduces Triamese-ViT, an innovative adaptation of the ViT model for brain age estimation. Tested on a dataset of 1351 MRI scans, Triamese-ViT achieves a Mean Absolute Error (MAE) of 3.84, a 0.9 Spearman correlation coefficient with chronological age, and a -0.29 Spearman coefficient correlation between the brain age gap and chronological age.
  arXiv  Detail & Related papers  (2024-01-13T03:29:56Z)
• Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI [0.0]
  Alzheimer's disease (AD) can be treated and managed if it is diagnosed early. In this study, we suggested using the Visual Transformer (ViT) and bi-LSTM to process MRI images for diagnosing Alzheimer's disease. The proposed method performs well in terms of accuracy, precision, F-score, and recall for the diagnosis of AD.
  arXiv  Detail & Related papers  (2024-01-06T06:11:03Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection [2.359557447960552]
  Vision transformers (ViT) have emerged in recent years as an alternative to CNNs for several computer vision applications. We tested variants of the ViT architecture for a range of desired neuroimaging downstream tasks based on difficulty. We achieved a performance boost of 5% and 9-10% upon fine-tuning vision transformer models pre-trained on synthetic and real MRI scans.
  arXiv  Detail & Related papers  (2023-03-14T20:18:12Z)
• Multimodal Gait Recognition for Neurodegenerative Diseases [38.06704951209703]
  We propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases. A new correlative memory neural network architecture is designed for extracting temporal features. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.
  arXiv  Detail & Related papers  (2021-01-07T10:17:11Z)

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.