[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119714-en":3,"doc-seo-119714-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119714,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Detection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine Learning - Paper abstract","Alzheimer's Disease is a devastating neurological disorder affecting elderly populations, making early and accurate detection essential for timely treatment and family support. The study presents a method to classify four stages of Alzheimer’s disease from MRI images using inertia tensor analysis and machine learning. A simple 2×2 moment-of-inertia-based matrix is computed from each scan, and features from the inertia tensor and its eigenvalues drive classification, achieving about 90% accuracy and offering cost-effective physical insight through image-dimension reduction.","arXiv :2304 . 13314v1 [ ee ss .IV] 26 Apr 2023  \nSpringer Nature 2021 LATEX template  \nDetection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine  \nLearning  \nKrishna Mahapatra 1 and Selvakumar R 1*  \n1 Department of Mathematics, Vellore Institute of Technology,  \nVellore, Tamil Nadu, India, 632014 .  \n*Corresponding author(s). E-mail(s): [rselvakumar@vit.ac.in](rselvakumar@vit.ac.in) ; Contributing authors: [ritamahapatra8556@gmail.com](ritamahapatra8556@gmail.com) ;  \nAbstract  \nAlzheimer's Disease is a devastating neurological disorder that is increasingly a􀀋ecting the elderly population. Early and accurate detection of Alzheimer's is crucial for providing e􀀋ective treatment and support for patients and their families. In this study, we present a novel approach for detecting four di􀀋erent stages of Alzheimer's disease from MRI scan images based on inertia tensor analysis and machine learning. From each available MRI scan image for di􀀋erent classes of Dementia, we 􀀌rst compute a very simple 2 􀀂 2 matrix, using the techniques of forming a moment of inertia tensor, which is largely used in different physical problems. Using the properties of the obtained inertia tensor and their eigenvalues, along with some other machine learning techniques, we were able to signi􀀌cantly classify the di􀀋erent types of Dementia. This process provides a new and unique approach to identifying and classifying di􀀋erent types of images using machine learning, with a classi􀀌cation accuracy of (90%) achieved. Our proposed method not only has the potential to be more cost-e􀀋ective than current methods but also provides a new physical insight into the disease by reducing the dimension of the image matrix. The results of our study highlight the potential of this approach for advancing the 􀀌eld of Alzheimer's disease detection and improving patient outcomes.  \nKeywords: Alzheimer's Disease, Magnetic Resonance Imaging, Inertia Tensor, Machine Learning.  \nSpringer Nature 2021 LATEX template  \n2 Inertia Tensor and Machine Learning  \n1 Introduction  \nAlzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by a decline in cognitive and behavioral abilities [1] . It is the most common cause of dementia among older adults and its prevalence is expected to increase as the population ages [2] .  \nIn 2006, approximately twenty-seven million people su􀀋ered from this disease worldwide [3] . AD is predicted to a􀀋ect 1 person in 85 people globally by 2050, and at least 43% of prevalent cases need high-level care for this disease [4] Figure1 . Although, the cause of AD is not completely understood [5] so far.Two kinds of AD are considered, one is sporadic and another one is familial. The sporadic AD ( 90 􀀀 95%) is genetical and environmental and late onset [6] . The risk factors of sporadic AD are i) age (1% for age 60-65, 50% for age over 85) and ii) gene (􀀀allele of apolipoprotein E gene, or APOE-) . The familial AD ( 5-10%) is early onset and some inherited dominant gene speed up the progression of the disease. There is no proven and modifying treatment for AD, a progressive, irreversible brain  \n| 'year_data.dat' using 1:2  |  |  |  |\n| --- | --- | --- | --- |\n|  |  |  |  |\n\nYear  \n2050  \n2040  \n2030  \n20202015  \n20102005  \nFig. 1 Projected Growth of Alzheimer's Disease Worldwide: 2005-2050 .  \ndisorder characterized by a decline in cognitive science [7] . Therefore, the detection of Alzheimer's disease (AD) at an early stage is crucial for the management and treatment of the disease as well as for planning for the future care of patients and their families [8] .  \nTechniques for brain imaging can be used to non-invasively see the pharmacology, function, or structure of the brains [9] . Non-invasive neuroimaging techniques, including Magnetic Resonance Image (MRI), Positron Emission Tomography (PET), and MRI biomarkers, can be used to deduce the clinical mechanisms of AD. However, the cost and complexity of","cbCaieGsu39TBXeq","https://ap.wps.com/l/cbCaieGsu39TBXeq","pdf",903640,1,13,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Alzheimer’s disease overview and prevalence\n## Imaging methods and early detection challenges\n## Machine learning approaches for AD classification\n## Motivation for inertia tensor based representation","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets early and accurate detection of Alzheimer’s disease across four disease stages using MRI data, supporting treatment planning and patient care.\"},{\"question\":\"How does the proposed method use inertia tensor information?\",\"answer\":\"For each MRI image, it constructs a simple 2×2 moment-of-inertia tensor matrix and uses properties of the inertia tensor and its eigenvalues as features for machine learning classification.\"},{\"question\":\"What level of performance is reported?\",\"answer\":\"The method reports classification accuracy of about 90%, and claims potential cost advantages over existing approaches while providing physical insight via image-dimension reduction.\"}]","Detection of Alzheimer's Disease using MRI scans based on Inertia Tensor and Machine Learning - 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