A novel Alzheimer’s disease detection approach using GAN-based brain slice image enhancement

With the prevalence and the enormous societal consequence on health of Alzheimer’s disease (AD), diagnosis of AD and its prodromal form, mild cognitive impairment (MCI) is essential for patient care, and has been a research hotspot in recent years. Existing studies have applied machine learning methods to perform AD early diagnosis by analyzing various biomarkers. However, the difficulty in extracting the low-dimensional high-level brain features that accurately reflect main AD-related variations of anatomical brain structures becomes a bottleneck of the diagnosis performance in most of the existing researches. To overcome this bottleneck, this paper proposes a novel three-component adversarial network-based AD detection method (brain slice generative adversarial network for Alzheimer’s disease detection, BSGAN-ADD) to predict the disease category. BSGAN-ADD combines generative adversarial network (GAN)-based brain slice image enhancement and deep convolutional neural network (CNN)-based AD detection. In BSGAN-ADD, under the restriction of the discriminator, the generator learns to integrate the disease category feedbacks from classifier into 2D-brain slice image reconstruction process for image enhancement in the training phase. In the prediction phase, the stacked CNN layers in the generator are used to extract high-level brain features from category-enhanced 2D-brain slice images. And the classifier receives the extracted brain features to output the posterior probabilities of diseased states (Normal, AD and MCI). Experimental results on two real-world datasets (Alzheimer’s disease neuroimaging initiative, ANDI, Open Access Series of Imaging Studies OASIS) demonstrate that the new feature extraction process used in BSGAN-ADD can extract more representative high-level brain features to achieve a significant diagnosis performance gain compared with several typical methods.