Alzheimer's And Parkinson's Disease Classification Using Deep Learning Based On MRI: A Review

A. Suganya., S. Aarthy
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Abstract

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions.
基于MRI深度学习的阿尔茨海默病和帕金森病分类研究综述
神经退行性疾病目前对准确诊断和提供准确预后信息提出了挑战。阿尔茨海默病(AD)和帕金森病(PD)可能需要数年时间才能得到明确的诊断。由于发达国家人口老龄化的加剧,神经退行性疾病如AD和PD变得越来越普遍,因此需要新的技术和更准确的测试来改善和加快这些疾病早期的诊断程序。随着人工智能在医学领域的广泛应用,深度学习在基于MRI的计算机辅助AD和PD诊断中显示出巨大的前景。本文分析和评估了现有基于深度学习(DL)的方法的有效性,该方法使用使用各种模式(包括功能和结构MRI)获得的MRI数据来识别神经系统疾病。根据结论确定了当前的几个研究问题,以及几个潜在的未来研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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