Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (MOD), Non-Demented (ND), and Very Mild Dementia (VMD). The proposed DSR-GAN is implemented using a PyTorch library and uses a dataset of 6,400 MRI images. A super-resolution (SR) technique is applied to enhance the clarity and detail of the images, allowing the DSR-GAN to refine particular image features. The CNN model undergoes hyperparameter optimization and incorporates data augmentation strategies to maximize its efficiency. The normalized error matrix and area under ROC curve are used experimentally to evaluate the CNN's performance which achieved a testing accuracy of 99.22%, an area under the ROC curve of 100%, and an error rate of 0.0516. Also, the performance of the DSR-GAN is assessed using three different metrics: structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and multi-scale structural similarity index measure (MS-SSIM). The achieved SSIM score of 0.847, while the PSNR and MS-SSIM percentage are 29.30 dB and 96.39%, respectively. The combination of the DSR-GAN and CNN models provides a rapid and precise method to distinguish between various stages of Alzheimer's disease, potentially aiding professionals in the screening of AD cases.