Transfer Learning-Assisted Prognosis of Alzheimer's Disease and Mild Cognitive Impairment Using Structural-MRI

Yusera Farooq Khan, B. Kaushik, Bilal Ahmed Mir, Rahul Verma, Harsha Khandelwal
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引用次数: 1

Abstract

Alzheimer's is a neurodegenerative disease that damages human brain cells and causes dementia. When the brain cells gradually deteriorate, it leads to the inability to carry out everyday activities. While conventional machine learning (ML) has been shown to be efficient in assisting with AD diagnosis, relatively few research have examined the effectiveness of deep learning and transfer learning in this difficult challenge. We assessed the possibility of early recognition and prediction of Alzheimer's disease (AD) using pre-trained transfer-learning algorithms on structural brain MRI. Advances in artificial intelligence are assisting in the enhancement of early detection of Alzheimer's disease. Using open-source neuroimaging data, researchers have been able to construct programs that help in Alzheimer's diagnosis and prognosis. The presented study is based on an effective technique of applying transfer learning to classify the structural MRI (s-MRI) Axial brain scans by fine-tuning a pre-trained convolutional neural network (CNN), ResNet50, and VGG-16. We have taken s-MRI Axial data from an online available data repository Alzheimer's Disease Neuroimaging Initiative (ADNI). We implemented pre-trained model namely CNN, VGG-16 and ResNet50 trained on brain s-MRI axial scans to classify them into three classes: Cognitive Normal (CN), Mild cognitive impairment (MCI), and Alzheimer's disease (AD). Experiments show that ResNet50 outperformed CNN and VGG-60 with an accuracy of 95.30% on brain MRI axial scan for accurate and early prediction of AD and the onset of MCI.
结构磁共振成像对阿尔茨海默病和轻度认知障碍的迁移学习辅助预后
阿尔茨海默氏症是一种神经退行性疾病,会损害人类脑细胞并导致痴呆症。当脑细胞逐渐退化时,就会导致无法进行日常活动。虽然传统的机器学习(ML)已被证明在辅助AD诊断方面是有效的,但相对较少的研究已经检验了深度学习和迁移学习在这一困难挑战中的有效性。我们评估了在结构脑MRI上使用预先训练的迁移学习算法早期识别和预测阿尔茨海默病(AD)的可能性。人工智能的进步有助于提高对阿尔茨海默病的早期发现。利用开源的神经成像数据,研究人员已经能够构建有助于阿尔茨海默氏症诊断和预后的程序。本研究基于一种有效的技术,通过微调预训练卷积神经网络(CNN)、ResNet50和VGG-16,应用迁移学习对结构MRI (s-MRI)轴向脑扫描进行分类。我们从阿尔茨海默病神经成像倡议(ADNI)的在线可用数据库中获取了s-MRI轴向数据。我们实现了预训练模型,即CNN、VGG-16和ResNet50,对脑s-MRI轴向扫描进行训练,将其分为三类:认知正常(CN)、轻度认知障碍(MCI)和阿尔茨海默病(AD)。实验表明,ResNet50在脑MRI轴位扫描上准确、早期预测AD和MCI发病的准确率优于CNN和VGG-60,达到95.30%。
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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