مقاله انگلیسی رایگان در مورد تشخیص بیماری آلزایمر براساس تصاویر MRI ساختاری – هینداوی ۲۰۱۷

hindawi

 

مشخصات مقاله
ترجمه عنوان مقاله
تشخیص بیماری آلزایمر براساس تصاویر MRI ساختاری با استفاده از ماشین شبکه عصبی ساماندهی شده و ویژگی های PCA
عنوان انگلیسی مقاله Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features
انتشار مقاله سال ۲۰۱۷
تعداد صفحات مقاله انگلیسی ۱۲ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه هینداوی
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
فرمت مقاله انگلیسی  PDF
رشته های مرتبط پزشکی، مهندسی کامپیوتر، فناوری اطلاعات
گرایش های مرتبط مغز و اعصاب، رادیولوژی، هوش مصنوعی، شبکه های کامپیوتری
نوع ارائه مقاله
ژورنال
مجله / کنفرانس مجله مهندسی بهداشت و درمان – Journal of Healthcare Engineering
دانشگاه National Research Center for Dementia – Gwangju – Republic of Korea
شناسه دیجیتال – doi
https://doi.org/10.1155/2017/5485080
کد محصول E10423
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

فهرست مطالب مقاله:
۱ Introduction
۲ Materials for Study
۳ Proposed Methods: Classification of Stages of AD Progression
۴ Experimental Results and Analysis
۵ Conclusions and Future Work
References

 

بخشی از متن مقاله:
Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy scorebased feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

Introduction

Alzheimer’s disease (AD) is a slow fatal neurodegenerative disease affecting people over the age of 65 years [1], while early-onset AD is also diagnosed before 65. The deposition of two abnormal protein fragments known as plagues and tangles in the brain causes the death of neuron cells. The hippocampus, where the memories are first formed, is the initially affected region by AD, and thus early symptoms of AD include memory problems resulting difficulties in word finding and thinking processes [2]. AD patients suffer from a lack of initiative, changes in personality or behavior in day-to-day functions at home, or at work, and in taking care of oneself, eventually, leading to death. The brain volume reduces dramatically through time and affects most of its functions with the progression of AD. With the increase in the population of elderly people in developed countries, AD is going to be a major problem in socioeconomic implications. According to the recent report [3], it is expected that the number of affected people will be doubled in the next 20 years and one in two aged above 85 years will suffer from AD by 2050. Thus, accurate diagnosis of AD is very important, especially, at its early stage. Conventionally, the diagnosis of AD is performed by a neuropsychological examination in support of structural imaging. It is reported in [4] that (1) in the early stage of AD, degeneration of neurons takes place in the medial temporal lobe, (2) gradually affecting the entorhinal cortex, the hippocampus, and the limbic system, and (3) neocortical areas are affected at the final stage. Therefore, the study of medial temporal lobe atrophy (MTA), particularly in the hippocampus, the entorhinal cortex, and the amygdala provides the evidence of the progression of AD. Generally, MTA is measured in terms of voxel-based [5], vertex-based [6], and ROI-based [7] approaches. However, as the disease progresses, other regions in the brain are also affected. In such cases, whole-brain methods are preferred rather than a specific region-based method; then, the characterization of brain atrophy for differentiating AD and MCI patients can be performed more efficiently.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد. بخش‌های موردنیاز علامت‌گذاری شده‌اند *