Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review
4
Zitationen
4
Autoren
2025
Jahr
Abstract
DL has considerably improved the accuracy and reliability of AD diagnosis with neuroimaging. Regardless of issues with data accessibility and adaptability, current studies into the interpretability of models and multimodal fusion provide potential for clinical application. Further research should concentrate on standardized data sets, rigorous validation architectures, and understandable AI methodologies to enhance the effectiveness of DL methods in AD prediction.
Ähnliche Arbeiten
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
2018 · 6.331 Zit.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2014 · 6.219 Zit.
Brain tumor segmentation with Deep Neural Networks
2016 · 3.173 Zit.
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
2016 · 2.604 Zit.
Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
2017 · 2.482 Zit.