College of Nursing, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.
Sensors (Basel). 2022 Jun 2;22(11):4250. doi: 10.3390/s22114250.
Alzheimer's disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer's disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer's patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer's Dataset (four classes of images) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer's disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer's Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.
阿尔茨海默病（AD）是一种影响老年人的慢性疾病。痴呆症有很多不同的类型，但阿尔茨海默病是导致死亡的主要原因之一。AD 是一种慢性大脑紊乱，会导致语言、定向、情绪波动、身体机能、记忆丧失、认知能力下降、情绪或性格变化，最终因痴呆而死亡。不幸的是，目前还没有针对它的治愈方法，而且它的病因也不清楚。临床上，成像工具可以辅助诊断，而深度学习最近成为这些工具的一个重要组成部分。深度学习几乎不需要对图像进行预处理，可以从原始图像中推断出最佳的数据表示，而无需事先进行特征选择。因此，它们产生了一个更客观、偏见更小的过程。卷积神经网络（CNN）的性能主要受所选超参数和使用的数据集的影响。利用迁移学习和 Gorilla Troops 对深度神经网络进行优化，建立了用于阿尔茨海默病患者分类的深度学习模型，用于早期诊断。本研究提出了一种用于 MRI 图像分类和 AD 检测的 A3C-TL-GTO 框架。A3C-TL-GTO 是一种用于 AD 分类的经验性定量框架，它是在 Alzheimer's Dataset（四类图像）和 Alzheimer's Disease Neuroimaging Initiative（ADNI）数据集上开发和评估的。该框架减少了分类器模型和数据集使用中的预处理步骤和超参数优化的偏差和可变性。我们的方法在 MRI 上进行了评估，很容易适应其他成像方法。根据我们的发现，该框架是完成这项任务的优秀工具，对患者护理有很大的潜在优势。ADNI 数据集是一个关于阿尔茨海默病的在线数据集，用于获取磁共振成像（MRI）大脑图像。实验结果表明，该框架在 Alzheimer's Dataset 上的准确率达到 96.65%，在 ADNI 数据集上的准确率达到 96.25%。此外，在准确性方面的表现优于其他最先进的方法。
