Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease

Almufareh, Maram Fahaad; Tehsin, Samabia; Humayun, Mamoona; Kausar, Sumaira

doi:10.3390/healthcare11202763

Cited by 10 publications

(1 citation statement)

References 42 publications

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“…The following studies have contributed to this ongoing exploration. For example, in [19], the research proposed an attention-based mechanism that employs the ViT approach for Alzheimer's disease detection using MRI images. In a similar vein, in [20], the research aimed to enhance the automatic detection of dementia in MRI brain data by investigating three prominent deep convolutional models (ResNet, DenseNet, and EfficientNet) along with two transformer-based architectures (MAE and DeiT) for mapping input images to clinical diagnoses.…”

Section: Related Workmentioning

confidence: 99%

Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach

Abbaoui,

Retal,

Ziti

et al. 2024

JCM

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Background: This study evaluates the performance of a vision transformer (ViT) model, ViT-b16, in classifying ischemic stroke cases from Moroccan MRI scans and compares it to the Visual Geometry Group 16 (VGG-16) model used in a prior study. Methods: A dataset of 342 MRI scans, categorized into ‘Normal’ and ’Stroke’ classes, underwent preprocessing using TensorFlow’s tf.data API. Results: The ViT-b16 model was trained and evaluated, yielding an impressive accuracy of 97.59%, surpassing the VGG-16 model’s 90% accuracy. Conclusions: This research highlights the ViT-b16 model’s superior classification capabilities for ischemic stroke diagnosis, contributing to the field of medical image analysis. By showcasing the efficacy of advanced deep learning architectures, particularly in the context of Moroccan MRI scans, this study underscores the potential for real-world clinical applications. Ultimately, our findings emphasize the importance of further exploration into AI-based diagnostic tools for improving healthcare outcomes.

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Section: Related Workmentioning

confidence: 99%

Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach

Abbaoui,

Retal,

Ziti

et al. 2024

JCM

View full text Add to dashboard Cite

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Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Shah,

Khan,

Rizwan

et al. 2024

Pattern Anal Applic

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Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known cognitive disorders. Early detection and treatment of cognitive diseases using artificial intelligence can help contain them. However, the complex spatial relationships and long-range dependencies found in medical imaging data present challenges in achieving the objective. Moreover, for a few years, the application of transformers in imaging has emerged as a promising area of research. A reason can be transformer’s impressive capabilities of tackling spatial relationships and long-range dependency challenges in two ways, i.e., (1) using their self-attention mechanism to generate comprehensive features, and (2) capture complex patterns by incorporating global context and long-range dependencies. In this work, a Bi-Vision Transformer (BiViT) architecture is proposed for classifying different stages of AD, and multiple types of cognitive disorders from 2-dimensional MRI imaging data. More specifically, the transformer is composed of two novel modules, namely Mutual Latent Fusion (MLF) and Parallel Coupled Encoding Strategy (PCES), for effective feature learning. Two different datasets have been used to evaluate the performance of proposed BiViT-based architecture. The first dataset contain several classes such as mild or moderate demented stages of the AD. The other dataset is composed of samples from patients with AD and different cognitive disorders such as mild, early, or moderate impairments. For comprehensive comparison, a multiple transfer learning algorithm and a deep autoencoder have been each trained on both datasets. The results show that the proposed BiViT-based model achieves an accuracy of 96.38% on the AD dataset. However, when applied to cognitive disease data, the accuracy slightly decreases below 96% which can be resulted due to smaller amount of data and imbalance in data distribution. Nevertheless, given the results, it can be hypothesized that the proposed algorithm can perform better if the imbalanced distribution and limited availability problems in data can be addressed. Graphical abstract

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Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

Hcini,

Jdey,

Dhahri

2024

Neural Process Lett

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Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides a comprehensive analysis of the use of deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), for the classification of AD using brain imaging data. While previous reviews have covered similar topics, this paper offers a unique perspective by providing a detailed comparison of CNN and ViT for AD classification, highlighting the strengths and limitations of each approach. Additionally, the review presents an updated and thorough analysis of the most recent studies in the field, including the latest advancements in CNN and ViT architectures, training methods, and performance evaluation metrics. Furthermore, the paper discusses the ethical considerations and challenges associated with the use of deep learning models for AD classification, such as the need for interpretability and the potential for bias. By addressing these issues, this review aims to provide valuable insights for future research and clinical applications, ultimately advancing the field of AD classification using deep learning techniques.

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Artificial Cognition for Detection of Mental Disability: A Vision Transformer Approach for Alzheimer’s Disease

Cited by 10 publications

References 42 publications

Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach

Automated Ischemic Stroke Classification from MRI Scans: Using a Vision Transformer Approach

Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Investigating Deep Learning for Early Detection and Decision-Making in Alzheimer’s Disease: A Comprehensive Review

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