AD-Lite Net:从核磁共振成像图像中检测阿尔茨海默氏症的轻量级简并 CNN 模型

Santanu Roy, Archit Gupta, Shubhi Tiwari, Palak Sahu
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摘要

阿尔茨海默病(Alzheimer's Disease,AD)是一种无法治愈的渐进性神经退行性疾病,会影响人的大脑,导致记忆力和认知能力下降,最终影响日常工作能力。因此,需要一种自动计算机辅助诊断(CAD)系统,它能在早期阶段更准确地检测出阿尔茨海默病。在这项研究中,我们提出了一个新颖的 AD-Lite Net 模型(从零开始训练),可以缓解上述问题。本研究的新颖之处在于:(I) 我们提出了一种非常轻量级的 CNN 模型,该模型包含深度可分离卷积(DWSC)层和全局平均池化(GAP)层。该模块由一个转换层(Tx-layer)和两个卷积层(concatenated with the original base model)组成。Tx 层将特征转换成非常独特的特征,而这些特征对于阿尔茨海默病来说是必不可少的。因此,所提出的具有 "并行合并 "功能的 AD-Lite Net 模型收敛速度更快,并能以非常通用的方式自动缓解核磁共振成像数据集的类别不平衡问题。为了证明我们提出的模型的有效性,我们在三个不同的磁共振成像数据集上实施了该模型。此外,我们还结合了 ADNI 和 AD 数据集,随后进行了 10 倍交叉验证实验,以验证模型的泛化能力。广泛的实验结果表明,我们提出的模型性能明显优于现有的所有 CNN 模型和最近流行的一个视觉转换器(ViT)模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AD-Lite Net: A Lightweight and Concatenated CNN Model for Alzheimer's Detection from MRI Images
Alzheimer's Disease (AD) is a non-curable progressive neurodegenerative disorder that affects the human brain, leading to a decline in memory, cognitive abilities, and eventually, the ability to carry out daily tasks. Manual diagnosis of Alzheimer's disease from MRI images is fraught with less sensitivity and it is a very tedious process for neurologists. Therefore, there is a need for an automatic Computer Assisted Diagnosis (CAD) system, which can detect AD at early stages with higher accuracy. In this research, we have proposed a novel AD-Lite Net model (trained from scratch), that could alleviate the aforementioned problem. The novelties we bring here in this research are, (I) We have proposed a very lightweight CNN model by incorporating Depth Wise Separable Convolutional (DWSC) layers and Global Average Pooling (GAP) layers. (II) We have leveraged a ``parallel concatenation block'' (pcb), in the proposed AD-Lite Net model. This pcb consists of a Transformation layer (Tx-layer), followed by two convolutional layers, which are thereby concatenated with the original base model. This Tx-layer converts the features into very distinct kind of features, which are imperative for the Alzheimer's disease. As a consequence, the proposed AD-Lite Net model with ``parallel concatenation'' converges faster and automatically mitigates the class imbalance problem from the MRI datasets in a very generalized way. For the validity of our proposed model, we have implemented it on three different MRI datasets. Furthermore, we have combined the ADNI and AD datasets and subsequently performed a 10-fold cross-validation experiment to verify the model's generalization ability. Extensive experimental results showed that our proposed model has outperformed all the existing CNN models, and one recent trend Vision Transformer (ViT) model by a significant margin.
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