Alinsaif Sadiq
College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin 39524, Saudi Arabia.
Comput Biol Med. 2025 Feb;185:109538. doi: 10.1016/j.compbiomed.2024.109538. Epub 2024 Dec 13.
Alzheimer's dementia (AD) is a neurodegenerative disorder that affects the central nervous system, causing the cells to stop working or die. The quality of life for individuals with AD steadily declines over time. While current treatments can relieve symptoms, a definitive cure remains elusive. However, technological advancements in machine learning (ML) and deep learning (DL) have opened up new possibilities for early AD detection. Early diagnosis is crucial, as trial drugs show promising results in patients who are diagnosed early. This study used a magnetic resonance imaging (MRI) dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The dataset consisted of 200 patients who were followed up at different time points and categorized as having AD (50), progressive-mild cognitive impairment to AD (50), stable-mild cognitive impairment (50), or cognitively normal (50). However, the utilization of MRI datasets poses challenges such as high dimensionality, limited training samples, and variability within and between subjects. To overcome these challenges, I propose using convolutional neural networks (CNNs) to extract informative features from an MRI sample. I fine-tune four pretrained models (i.e., SqueezeNet-v1.1, MobileNet-v2, Xception, and Inception-v3) to generate discriminative descriptors of MRI sample characteristics. Additionally, I suggest using the 3D shearlet transform, considering the volumetric properties of MRI data. Before the transformation, I implemented preprocessing protocols such as skull stripping, normalization of image intensity, and spatial cropping. I then summarize the shearlet coefficients using texture-based techniques. Finally, I integrate both deep and shearlet-based features using discriminant correlation analysis (DCA) to yield a robust and computationally efficient classification model. I employ two classifiers, support vector machines (SVMs) and decision tree baggers (DTBs). My objective was to develop a model capable of accurately diagnosing early-stage AD that can facilitate effective intervention and management of the condition. Our feature representation demonstrated high accuracy when applied to AD datasets at three time points. Specifically, accuracies of 94.46%, 92.97%, and 95.44% were achieved 18 months, 12 months, and at the time of stable diagnosis, respectively.
阿尔茨海默病性痴呆（AD）是一种影响中枢神经系统的神经退行性疾病，会导致细胞停止工作或死亡。随着时间的推移，AD患者的生活质量会稳步下降。虽然目前的治疗方法可以缓解症状，但仍难以找到根治方法。然而，机器学习（ML）和深度学习（DL）的技术进步为AD的早期检测开辟了新的可能性。早期诊断至关重要，因为试验药物在早期诊断的患者中显示出有希望的结果。本研究使用了来自阿尔茨海默病神经影像学倡议（ADNI）的磁共振成像（MRI）数据集。该数据集由200名患者组成，他们在不同时间点接受随访，并被分类为患有AD（50例）、从轻度认知障碍进展为AD（50例）、稳定的轻度认知障碍（50例）或认知正常（50例）。然而，MRI数据集的利用带来了诸如高维度、训练样本有限以及个体内部和个体之间的变异性等挑战。为了克服这些挑战，我建议使用卷积神经网络（CNN）从MRI样本中提取信息特征。我对四个预训练模型（即SqueezeNet-v1.1、MobileNet-v2、Xception和Inception-v3）进行微调，以生成MRI样本特征的判别描述符。此外，考虑到MRI数据的体积特性，我建议使用3D剪切波变换。在变换之前，我实施了诸如颅骨剥离、图像强度归一化和空间裁剪等预处理协议。然后，我使用基于纹理的技术总结剪切波系数。最后，我使用判别相关分析（DCA）整合基于深度和剪切波的特征，以产生一个强大且计算高效的分类模型。我使用了两个分类器，支持向量机（SVM）和决策树装袋器（DTB）。我的目标是开发一个能够准确诊断早期AD的模型，以促进对该疾病的有效干预和管理。当我们的特征表示应用于三个时间点的AD数据集时，显示出了很高的准确性。具体而言，在18个月、12个月和稳定诊断时，准确率分别达到了94.46%、92.97%和95.44%。
