Ensemble Method for Early Alzheimer's Detection from PET Scans
Using deep-learning models and ensemble techniques, accurate early detection of Alzheimer's disease is achievable with high precision and recall rates.
A Hybrid Deep Learning Approach (AlzhiNet) for Early Alzheimer's Disease Detection Using 2D and 3D Convolutional Neural Networks
Combining 2D and 3D convolutional neural networks in a novel framework called AlzhiNet significantly improves the accuracy of early Alzheimer's disease diagnosis using MRI data.
Multimodal Ensemble Machine Learning Approach Improves Early Detection of Alzheimer's Disease Using Class Balancing
Integrating multiple data modalities and addressing class imbalance in datasets significantly enhances the accuracy of machine learning models in diagnosing and predicting the progression of Alzheimer's disease, particularly in early detection scenarios.
Hybrid Transformer Model for Early Alzheimer's Disease Detection Using Handwriting Analysis: Integrating 2D Image and 1D Signal Features
This research proposes a novel hybrid Transformer model, HSDA-MS Transformer, which integrates 2D handwriting images and 1D dynamic signal features for improved early detection of Alzheimer's Disease, outperforming existing state-of-the-art classifiers.
