An Analysis of Deep Learning Techniques in Neuroimaging

Narmatha C, Hayam Alatawi, H. Alatawi
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引用次数: 3

Abstract

Deep learning is a machine learning technique that has demonstrated better results and performance when compared to standard machine learning algorithms in relation to higher dimensional MRI brain imaging data. The applications of deep learning in the clinical domain are discussed in this study. A detailed analysis of several deep learning algorithms for the Alzheimer's disease diagnosis is analyzed, in which this disorder of brain that gradually spreads and destroys memory of the brain, and it is a typical disorder in elderly individuals due to dementia. When it comes to brain image processing, the most commonly used and represented method, according to most research publications, is Convolutional Neural Networks (CNN). Following a review of many relevant studies for the Alzheimer's disease diagnosis, it was shown that utilizing advanced deep learning algorithms in different datasets (OASIS and ADNI) combined to one can improve AD prediction at earlier stages.
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神经影像学中的深度学习技术分析
深度学习是一种机器学习技术,与标准机器学习算法相比,在高维MRI脑成像数据方面表现出更好的结果和性能。本研究讨论了深度学习在临床领域的应用。详细分析了几种用于阿尔茨海默病诊断的深度学习算法。阿尔茨海默病是一种逐渐扩散并破坏大脑记忆的大脑紊乱,是老年个体因痴呆引起的典型紊乱。当涉及到大脑图像处理时,根据大多数研究出版物,最常用和代表性的方法是卷积神经网络(CNN)。在回顾了许多与阿尔茨海默病诊断相关的研究后,研究表明,在不同的数据集(OASIS和ADNI)中结合使用先进的深度学习算法可以提高早期阿尔茨海默病的预测。
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