A Robust Deep Vision Transformer Model For Early Diagnosis Of Alzheimers Disease

A progressive neurological disease, Alzheimer's disease (AD) is identified by cognitive decline and memory loss.Effective intervention and better patient outcomes depend on early diagnosis.In this work, the application of Vision Transformers (ViTs) to MRI scans for early AD identification is investigated.Compared to conventional architectures, ViTs offer advantages by utilizing self-attention processes to gather global picture features.The model was developed and evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset that cover four stages of disease which are Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD).The results demonstrate that the ViT model outperforms conventional Convolutional Neural Networks (CNNs) like DenseNet-121 and VGG-16, achieving 95% accuracy and a 96% F1-score.It reveals strong performance in detecting early-stage AD and offers interpretability through attention maps highlighting key brain regions.This study underscores the potential of ViT-based models to enhance diagnostic accuracy and clinical decision-making in Alzheimer's disease.Key words: -Alzheimer's disease (AD), Vision Transformers (ViTs), early diagnosis, Mild cognitive Impairment (MCI) I Overview Alzheimer's disease (AD) is the major cause of dementia, accounting for 60-80% of cases worldwide [1].It primarily affects elderly people, causing gradual cognitive deterioration and functional reliance.With more than 55 million individuals already impacted and forecasts of 139 million by 2050 [2], the need for novel diagnostic methods is growing.Despite extensive study, there is no cure, and current medications only address symptoms [3].Early detection is therefore critical for allowing prompt treatments and improving patient outcomes [4].Structural Magnetic Resonance Imaging (MRI) is frequently utilized to detect early symptoms of AD, including brain shrinkage in areas such as the hippocampus [5].Manual interpretation, on the other hand, is time-consuming and unreliable.To address these constraints, artificial intelligence (AI) and deep learning techniques, particularly Convolutional Neural Networks (CNNs), have been frequently used to automate MRI processing [6,7].While successful, CNNs fail to capture long-term dependencies and frequently require huge labeled datasets [8].Vision Transformers (ViTs) provide an alternative by using self-attention procedures to represent global relationships between picture patches [9].Recently, ViTs have shown remarkable performance in vision tasks, such as medical imaging, despite their initial design for natural language processing [10], [11].They are intriguing candidates for improving AD diagnosis because of their capacity to represent global environment and flexibility in transfer learning.Even with advances in deep learning, it is still difficult to diagnose AD using MRI in a timely and reliable manner.CNNs need a lot of data and have limited ability to capture global properties.Though their use in this situation is still in its infancy, vision transformers could provide an answer.The purpose of this work is to create and assess a ViT-based model that can more accurately and broadly categorize MRI scans into AD and non-AD groups.To increase diagnostic precision and model interpretability, this study investigates the application of Vision Transformers for structural MRI-based early Alzheimer's disease diagnosis.This work aims to create and construct a ViT model for brain MRI image classification, check the viability of employing Vision Transformers for early Alzheimer's disease detection, and compare its performance to that of conventional CNNs using a range of diagnostic measures.In order to improve interpretability and provide insightful information for the creation of AI-assisted neuroimaging diagnostic tools, the study also intends to examine attention maps.The goal of the project is to utilize the ADNI dataset to categorize MRI scans into four groups: cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD).Several criteria are used to assess the model's performance, and CNNbased models are contrasted with it.Limitations include processing limits, class imbalances, and difficulties generalizing to real-world clinical data.