Ali Imad, Saleem Nasir, Alhussein Musaed, Zohra Benazeer, Aurangzeb Khursheed, Haq Qazi Mazhar Ul
Department of Computer Science, University of Swat, Swat, KP, Pakistan.
Department of Electrical Engineering, Faculty of Engineering & Technology (FET), Gomal University, Dera Ismail Khan, Pakistan.
Front Med (Lausanne). 2024 Aug 29;11:1443151. doi: 10.3389/fmed.2024.1443151. eCollection 2024.
Alzheimer's disease (AD) is a neurodegenerative disorder and the most prevailing cause of dementia. AD critically disturbs the daily routine, which usually needs to be detected at its early stage. Unfortunately, AD detection using magnetic resonance imaging is challenging because of the subtle physiological variations between normal and AD patients visible on magnetic resonance imaging.
To cope with this challenge, we propose a deep convolutional generative adversarial network (DeepCGAN) for detecting early-stage AD in this article. The DeepCGAN is an unsupervised generative model that expands the dataset size in addition to its diversity by utilizing the generative adversarial network (GAN). The Generator of GAN follows the encoder-decoder framework and takes cognitive data as inputs, whereas the Discriminator follows a structure similar to the Generator's encoder. The last dense layer uses a softmax classifier to detect the labels indicating the AD.
The proposed model attains an accuracy rate of 97.32%, significantly surpassing recent state-of-the-art models' performance, including Adaptive Voting, ResNet, AlexNet, GoogleNet, Deep Neural Networks, and Support Vector Machines.
The DeepCGAN significantly improves early AD detection accuracy and robustness by enhancing the dataset diversity and leveraging advanced GAN techniques, leading to better generalization and higher performance in comparison to traditional and contemporary methods. These results demonstrate the ecacy of DeepCGAN in enhancing early AD detection, thereby potentially improving patient outcomes through timely intervention.
阿尔茨海默病（AD）是一种神经退行性疾病，也是痴呆最常见的病因。AD严重干扰日常生活，通常需要在早期阶段进行检测。不幸的是，由于在磁共振成像上正常人和AD患者之间细微的生理差异，使用磁共振成像检测AD具有挑战性。
为应对这一挑战，我们在本文中提出了一种用于检测早期AD的深度卷积生成对抗网络（DeepCGAN）。DeepCGAN是一种无监督生成模型，除了利用生成对抗网络（GAN）增加数据集的多样性外，还能扩大数据集的规模。GAN的生成器遵循编码器-解码器框架，以认知数据作为输入，而判别器遵循与生成器的编码器类似的结构。最后一个全连接层使用softmax分类器来检测表示AD的标签。
所提出的模型达到了97.32%的准确率，显著超过了包括自适应投票、残差网络、AlexNet、谷歌网络、深度神经网络和支持向量机在内的近期最先进模型的性能。
DeepCGAN通过增强数据集的多样性和利用先进的GAN技术，显著提高了早期AD检测的准确性和鲁棒性，与传统和当代方法相比，具有更好的泛化能力和更高的性能。这些结果证明了DeepCGAN在增强早期AD检测方面的有效性，从而有可能通过及时干预改善患者的预后。
