3D-TDA -- Topological feature extraction from 3D images for Alzheimer's disease classification

Now that disease-modifying therapies for Alzheimer disease have been approved by regulatory agencies, the early, objective, and accurate clinical diagnosis of AD based on the lowest-cost measurement modalities possible has become an increasingly urgent need. In this study, we propose a novel feature extraction method using persistent homology to analyze structural MRI of the brain. This approach converts topological features into powerful feature vectors through Betti functions. By integrating these feature vectors with a simple machine learning model like XGBoost, we achieve a computationally efficient machine learning model. Our model outperforms state-of-the-art deep learning models in both binary and three-class classification tasks for ADNI 3D MRI disease diagnosis. Using 10-fold cross-validation, our model achieved an average accuracy of 97.43 percent and sensitivity of 99.09 percent for binary classification. For three-class classification, it achieved an average accuracy of 95.47 percent and sensitivity of 94.98 percent. Unlike many deep learning models, our approach does not require data augmentation or extensive preprocessing, making it particularly suitable for smaller datasets. Topological features differ significantly from those commonly extracted using convolutional filters and other deep learning machinery. Because it provides an entirely different type of information from machine learning models, it has the potential to combine topological features with other models later on.

Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI

Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide, necessitating early and accurate diagnosis for optimal patient management. In recent years, advancements in deep learning have shown remarkable potential in medical image analysis. Methods In this study, we present "ViTranZheimer," an AD diagnosis approach which leverages video vision transformers to analyze 3D brain MRI data. By treating the 3D MRI volumes as videos, we exploit the temporal dependencies between slices to capture intricate structural relationships. The video vision transformer's self-attention mechanisms enable the model to learn long-range dependencies and identify subtle patterns that may indicate AD progression. Our proposed deep learning framework seeks to enhance the accuracy and sensitivity of AD diagnosis, empowering clinicians with a tool for early detection and intervention. We validate the performance of the video vision transformer using the ADNI dataset and conduct comparative analyses with other relevant models. Results The proposed ViTranZheimer model is compared with two hybrid models, CNN-BiLSTM and ViT-BiLSTM. CNN-BiLSTM is the combination of a convolutional neural network (CNN) and a bidirectional long-short-term memory network (BiLSTM), while ViT-BiLSTM is the combination of a vision transformer (ViT) with BiLSTM. The accuracy levels achieved in the ViTranZheimer, CNN-BiLSTM, and ViT-BiLSTM models are 98.6%, 96.479%, and 97.465%, respectively. ViTranZheimer demonstrated the highest accuracy at 98.6%, outperforming other models in this evaluation metric, indicating its superior performance in this specific evaluation metric. Conclusion This research advances the understanding of applying deep learning techniques in neuroimaging and Alzheimer's disease research, paving the way for earlier and less invasive clinical diagnosis.