Pandey Purushottam Kumar, Pruthi Jyoti, Alzahrani Saeed, Verma Anshul, Zohra Benazeer
Department of Computer Science, Manav Rachna University, Faridabad, Haryana, India.
Management Information System Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia.
Front Med (Lausanne). 2024 Sep 20;11:1445325. doi: 10.3389/fmed.2024.1445325. eCollection 2024.
Neurodegenerative disorders such as Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) significantly impact brain function and cognition. Advanced neuroimaging techniques, particularly Magnetic Resonance Imaging (MRI), play a crucial role in diagnosing these conditions by detecting structural abnormalities. This study leverages the ADNI and OASIS datasets, renowned for their extensive MRI data, to develop effective models for detecting AD and MCI. The research conducted three sets of tests, comparing multiple groups: multi-class classification (AD vs. Cognitively Normal (CN) vs. MCI), binary classification (AD vs. CN, and MCI vs. CN), to evaluate the performance of models trained on ADNI and OASIS datasets. Key preprocessing techniques such as Gaussian filtering, contrast enhancement, and resizing were applied to both datasets. Additionally, skull stripping using U-Net was utilized to extract features by removing the skull. Several prominent deep learning architectures including DenseNet-201, EfficientNet-B0, ResNet-50, ResNet-101, and ResNet-152 were investigated to identify subtle patterns associated with AD and MCI. Transfer learning techniques were employed to enhance model performance, leveraging pre-trained datasets for improved Alzheimer's MCI detection. ResNet-101 exhibited superior performance compared to other models, achieving 98.21% accuracy on the ADNI dataset and 97.45% accuracy on the OASIS dataset in multi-class classification tasks encompassing AD, CN, and MCI. It also performed well in binary classification tasks distinguishing AD from CN. ResNet-152 excelled particularly in binary classification between MCI and CN on the OASIS dataset. These findings underscore the utility of deep learning models in accurately identifying and distinguishing neurodegenerative diseases, showcasing their potential for enhancing clinical diagnosis and treatment monitoring.
诸如阿尔茨海默病（AD）和轻度认知障碍（MCI）等神经退行性疾病会对脑功能和认知产生重大影响。先进的神经成像技术，尤其是磁共振成像（MRI），通过检测结构异常在诊断这些病症中发挥着关键作用。本研究利用以其广泛的MRI数据而闻名的ADNI和OASIS数据集，来开发用于检测AD和MCI的有效模型。该研究进行了三组测试，比较多个组：多类分类（AD与认知正常（CN）与MCI）、二元分类（AD与CN，以及MCI与CN），以评估在ADNI和OASIS数据集上训练的模型的性能。高斯滤波、对比度增强和调整大小等关键预处理技术被应用于这两个数据集。此外，使用U-Net进行颅骨剥离以通过去除颅骨来提取特征。研究了包括DenseNet-201、EfficientNet-B0、ResNet-50、ResNet-101和ResNet-152在内的几种著名深度学习架构，以识别与AD和MCI相关的细微模式。采用迁移学习技术来提高模型性能，利用预训练数据集来改进阿尔茨海默病MCI检测。与其他模型相比，ResNet-101表现出卓越的性能，在包含AD、CN和MCI的多类分类任务中，在ADNI数据集上达到了98.21%的准确率，在OASIS数据集上达到了97.45%的准确率。它在区分AD与CN的二元分类任务中也表现良好。ResNet-152在OASIS数据集上MCI与CN的二元分类中表现尤为出色。这些发现强调了深度学习模型在准确识别和区分神经退行性疾病方面的实用性，并展示了它们在加强临床诊断和治疗监测方面的潜力。
