Vaithianathan Krishnakumar, Pernabas Julian Benadit, Parthiban Latha, Rashid Mamoon, Alshamrani Sultan S
Department of Computer Engineering, Karaikal Polytechnic College, Varichikudy, Karaikal, Puducherry, India.
Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, India.
PeerJ Comput Sci. 2024 Nov 28;10:e2502. doi: 10.7717/peerj-cs.2502. eCollection 2024.
Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimer's disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis. In this study, a unique feature extraction technique based on normalized group activations that can be applied to both structural MRI and resting-state-fMRI (rs-fMRI) is proposed. This method is split into two phases: multi-trait condensed feature extraction networks and regional association networks. The initial phase involves extracting features from various brain regions using different multi-layered convolutional networks. Then, multiple regional association networks with normalized group activations for all the regional pairs are trained and the output of these networks is given as input to a classifier. To provide an unbiased estimate, an automated diagnosis system equipped with the proposed feature extraction is designed and analyzed on multi-cohort Alzheimer's Disease Neuroimaging Initiative (ADNI) data to predict multi-stages of AD. This system is also trained/tested on heterogeneous features such as non-transformed features, curvelets, wavelets, shearlets, textures, and scattering operators. Baseline scans of 185 rs-fMRIs and 1442 MRIs from ADNI-1, ADNI-2, and ADNI-GO datasets are used for validation. For MCI (mild cognitive impairment) classifications, there is an increase of 1-4% in performance. The outcome demonstrates the good discriminatory behaviour of the proposed features and its efficiency on rs-fMRI time-series and MRI data to classify multiple stages of AD.
开发了几种深度学习网络来识别阿尔茨海默病（AD）复杂的萎缩模式。在深度神经网络中使用的各种激活函数中，整流线性单元是使用最多的。尽管对这些函数进行了单独分析，但尚未探索用于神经影像分析的组激活及其解释。在本研究中，提出了一种基于归一化组激活的独特特征提取技术，该技术可应用于结构磁共振成像（MRI）和静息态功能磁共振成像（rs-fMRI）。该方法分为两个阶段：多特征浓缩特征提取网络和区域关联网络。初始阶段涉及使用不同的多层卷积网络从各个脑区提取特征。然后，针对所有区域对训练具有归一化组激活的多个区域关联网络，并将这些网络的输出作为分类器的输入。为了提供无偏估计，设计了一个配备所提出特征提取的自动诊断系统，并在多队列阿尔茨海默病神经影像倡议（ADNI）数据上进行分析，以预测AD的多个阶段。该系统还在诸如未转换特征、曲波、小波、剪切波、纹理和散射算子等异构特征上进行训练/测试。使用来自ADNI-1、ADNI-2和ADNI-GO数据集的185个rs-fMRI和1442个MRI的基线扫描进行验证。对于轻度认知障碍（MCI）分类，性能提高了1-4%。结果表明所提出特征具有良好的判别行为，并且其在rs-fMRI时间序列和MRI数据上对AD多个阶段进行分类的效率较高。
