Deep ensemble learning of sparse regression models for brain disease diagnosis

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Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “This is the case of (Suk et al., 2017) where the CNN is applied to the outputs of several regression models performed between MRI-based features and clinical scores with different hyperparameters.”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “Training from scratch has been applied to assessing the presence of Alzheimer’s disease based on brain MRI using deep learning.(35) In that study, using the publicly available ADNI cohort, sparse regression models were combined with deep neural networks to achieve higher classification performance c”
A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes (2018, background): “However, brain FC patterns analysis remains challenging due to the inherent high dimensionality of data and insuﬃcient sample sizes (Kim et al., 2016; Suk et al., 2017).”
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, methodology): “Furthermore, ensemble learning methods have been com-1264 bined with deep learning for different applications, includ-1265 ing human activity recognition [174], time series forecasting [175], disease prediction [137], [176], [177], wind speed prediction [178], and outlier detection [179].”