Chui Kwok Tai, Gupta Brij B, Alhalabi Wadee, Alzahrani Fatma Salih
Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China.
International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan.
Diagnostics (Basel). 2022 Jun 23;12(7):1531. doi: 10.3390/diagnostics12071531.
Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85−3.88%, 2.43−2.66%, and 1.8−40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.
阿尔茨海默病（AD）是最常见的痴呆类型（>60%），会对患者及其护理人员的心理和生理发展以及经济和社会发展造成严重破坏。由于医务人员短缺，AD的自动诊断对于减轻医务人员的工作量和提高医疗诊断的准确性变得更加重要。以常见的磁共振成像（MRI）扫描作为输入，使用卷积神经网络（CNN）设计了一种AD检测模型。为了加强超参数的微调，从而提高检测精度，引入了迁移学习（TL），它从异构数据集中引入领域知识。生成对抗网络（GAN）被应用于在基准数据集的少数类中生成额外的训练数据。使用三个基准（OASIS系列）数据集进行的性能评估和分析表明了所提方法的有效性，在GAN和TL的消融研究以及与现有工作的比较中，该方法分别将检测模型的准确率提高了2.85−3.88%、2.43−2.66%和1.8−40.1%。
