Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation

Abstract

With the evolution of deep learning technologies, computer vision-related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out-perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three-stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal-to-noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

“… Han et al (2021) reported the medical anomaly detection GAN (MAGAN) with an AUC of 0.89. Three studies showed higher accuracy of GAN-based methods (0.71, 0.85, and 0.83) ( Islam and Zhang, 2020 ; Shin et al, 2020 ; Sajjad et al, 2021 ).…”

“…Regarding the publication year, all the included articles were published between 2018 and 2021, and more than half of them (8/14) were published in 2021 ( Figure 2A ; Baydargil et al, 2021 ; Gao et al, 2021 ; Han et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ; Sajjad et al, 2021 ; Zhao et al, 2021 ; Zhou X. et al, 2021 ). Regarding the data source, neuroimaging data analyzed in 13 studies were mainly from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) ( Pan et al, 2018 ; Yan et al, 2018 ; Wegmayr et al, 2019 ; Islam and Zhang, 2020 ; Kim et al, 2020 ; Shin et al, 2020 ; Baydargil et al, 2021 ; Gao et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ; Sajjad et al, 2021 ; Zhao et al, 2021 ; Zhou X. et al, 2021 ), and some data were from the Open Access Series of Imaging Studies (OASIS) ( Han et al, 2021 ; Zhao et al, 2021 ), the Australian Imaging, Biomarker and Lifestyle Flagship Study of Aging (AIBL) and the National Alzheimer’s Coordinating Center (NACC) databases ( Figure 2B ; Zhou X. et al, 2021 ). Two studies established a test set from the collection of clinical data ( Wegmayr et al, 2019 ; Kim et al, 2020 ).…”

“…One study used MRI and other clinical data (age, sex, education level, and other parameters) ( Zhao et al, 2021 ). A deep convolutional GAN (DCGAN) was applied in 3 studies ( Islam and Zhang, 2020 ; Kang et al, 2021 ; Sajjad et al, 2021 ), and conditional GAN (CGAN) was applied in 2 studies ( Figure 2E ; Yan et al, 2018 ; Shin et al, 2020 ). The type of GAN in the remaining studies varied.…”

“…Two studies stimulated the process of brain aging observed in MRI images ( Wegmayr et al, 2019 ; Zhao et al, 2021 ). Two studies used GAN to augment imaging data and improve the training effects of the classifiers ( Islam and Zhang, 2020 ; Sajjad et al, 2021 ). Five studies achieved conversion between PET and MRI data to provide supplementary data ( Pan et al, 2018 ; Yan et al, 2018 ; Shin et al, 2020 ; Gao et al, 2021 ; Lin W. et al, 2021 ).…”

“…Fake images generated by this model, which highly resemble the real images, might exercise the same function as real images in disease diagnosis. In recent years, GAN has shown application value in diagnosing AD by providing image processing support, including quality improvement for low-dose PET images or 1.5-T MRIs ( Wang et al, 2018 ; Ouyang et al, 2019 ; Zhou X. et al, 2021 ), predicting brain images at a future time point ( Wegmayr et al, 2019 ; Zhao et al, 2021 ), data augmentation for network training ( Islam and Zhang, 2020 ; Sajjad et al, 2021 ), and interconversion of PET and MRI data ( Gao et al, 2021 ; Lin W. et al, 2021 ). With the GAN-based deep-learning classification framework, a more accurate diagnosis of AD is promising and may be achieved.…”