Alzheimer’s disease is one of the major challenges of population ageing, and diagnosis and prediction of the disease through various biomarkers is the key. While the application of deep learning as imaging technologies has recently expanded across the medical industry, empirical design of these technologies is very difficult. The main reason for this problem is that the performance of the Convolutional Neural Networks (CNN) differ greatly depending on the statistical distribution of the input dataset. Different hyperparameters also greatly affect the convergence of the CNN models. With this amount of information, selecting appropriate parameters for the network structure has became a large research area. Genetic Algorithm (GA), is a very popular technique to automatically select a high-performance network architecture. In this paper, we show the possibility of optimising the network architecture using GA, where its search space includes both network structure configuration and hyperparameters. To verify the performance of our Algorithm, we used an amyloid brain image dataset that is used for Alzheimer’s disease diagnosis. As a result, our algorithm outperforms Genetic CNN by $11.73\%$ on a given classification task.

Dementia is a degenerative disease that is increasingly prevalent in an aging society. Alzheimer’s disease (AD), the most common type of dementia, is best mitigated via early detection and management. Deep learning is an artificial intelligence technique that has been used to diagnose and predict diseases by extracting meaningful features from medical images. The convolutional neural network (CNN) is a representative application of deep learning, serving as a powerful tool for the diagnosis of AD. Recently, vision transformers (ViT) have yielded classification performance exceeding that of CNN in some diagnostic image classifications. Because the brain is a very complex network with interrelated regions, ViT, which captures direct relationships between images, may be more effective for brain image analysis than CNN. Therefore, we propose a method for classifying dementia images by applying 18F-Florbetaben positron emission tomography (PET) images to ViT. Data were evaluated via binary (normal control and abnormal) and ternary (healthy control, mild cognitive impairment, and AD) classification. In a performance comparison with the CNN, VGG19 was selected as the comparison model. Consequently, ViT yielded more effective performance than VGG19 in binary classification. However, in ternary classification, the performance of ViT cannot be considered excellent. These results show that it is hard to argue that the ViT model is better at AD classification than the CNN model.

In this study, the anomaly analysis of Alzheimer’s disease using positron emission tomography (PET) images using an unsupervised proposed adversarial model is investigated. The model consists of three parts: a parallel-network encoder, which is comprised of a convolutional pipeline and a dilated convolutional pipeline that extracts global and local features and concatenates them, a decoder that reconstructs the input image from the obtained feature vector, and a discriminator that distinguishes if the input image image is real or fake. The hypothesis is that if the proposed model is trained with only normal brain images, the corresponding construction loss for normal images should be minimal. However, if the input image belongs to a class that is designated as an anomaly that which the model is not trained with, then the construction loss will be high. This will reflect during the anomaly score comparison between the normal and the anomalous image. A multi-case analysis is performed for three major classes using the Alzheimer’s Disease Neuroimaging Initiative dataset, Alzheimer’s disease, mild cognitive impairment, and normal control. The base parallel-encoder network shows better classification accuracy than the benchmark models, and the proposed model that is built on the parallel model outperforms the benchmark anomaly detection models. The proposed model gave out 96.03% and 75.21% in classification and area under the curve score, respectively. Additionally, a qualitative evaluation done by using Fréchet inception distance gave a better score than the state-of-the-art by three points.

Purpose: We assessed the effect of home-based cognitive intervention (HCI) on cognitive function along with brain metabolism by 18F-FDG PET in patients with amnestic MCI (aMCI). Patients and methods: Fifty-seven patients with aMCI from three hospitals were randomized (30 HCI, 27 control). For 12 weeks, subjects received HCI. Thirty-two subjects (15 HCI, 17 control) underwent brain 18-F-FDG-PET imaging at baseline and at 12 and 24 weeks. Results: The HCI group showed significant improvement in the scores of the Controlled Oral Word Association Test (COWAT) 12 and at 24 weeks. Significant brain metabolic changes by 18F-FDG PET were not observed. Conclusion: The current study suggests that HCI was effective in improving general cognition along with frontal executive function in patients with aMCI.

In Alzheimer disease (AD), neuroinflammation is an important process related to the deposition of beta-amyloid plaques and the activation of microglia. The inflammatory process can occur in both the gray matter and the white matter. We evaluated glucose metabolism of the white matter in AD patients and compared the value with cognitive parameters of the patients. Eighteen AD patients and 18 healthy subjects underwent F-18 fluorodeoxyglucose (FDG) and F-18 florbetaben positron emission tomography (PET). After segmentation of the white matter in both PET images, the specific binding ratio (SBR) of the global and regional cerebral white matter was checked. We evaluated the differences in SBR of the global and regional white matter between AD patients and healthy subjects. Then, we assessed the correlation between SBR and cognitive parameters in AD patients. In F-18 FDG PET images, the global white matter SBR was significantly higher in AD patients than in healthy subjects. In the regional analysis, the white matter SBR was significantly higher for the frontal, temporal, and parietal areas in AD patients. In the correlation analysis with F-18 FDG PET, SBR was significantly correlated with the Global Deterioration Scale, Mini-Mental State Examination scores, and amyloid deposition. Glucose metabolism of the white matter was significantly higher in AD patients than in healthy subjects and it was related to the scores of cognitive parameters. We suggest that F-18 FDG PET, like 18-kDa translocator protein PET, could be used as an indicator of neuroinflammation; however, further research is needed for a direct comparison between the 2 tests.