Hybrid stacking of Squeeze Net features and ML models for accurate Alzheimer’s diagnosis | Synapse
March 3, 2026Open Access
Hybrid stacking of Squeeze Net features and ML models for accurate Alzheimer’s diagnosis
Key Points
Accurate diagnosis of Alzheimer’s was achieved through hybrid machine learning models and neural networks, enhancing performance.
The method reported an impressive 92% classification accuracy with a robust set of extracted Squeeze Net features.
Employing a hybrid stacking approach allowed for improved integration of features from deep learning techniques.
This analysis emphasizes the need for advanced diagnostic tools, as disease detection benefits from machine learning methods and neural network applications.