Recent natural mental health This study evaluated the development of ensemble deep learning (EDL) models used to characterize and estimate AD.
study: Ensemble deep learning for Alzheimer's disease characterization and estimation. Image credit: SewCreamStudio/Shutterstock.om
ensemble deep learning
EDL combines the outputs of multiple machine learning (ML) models to enhance generalization performance. Traditional approaches to building ensembles use deep neural networks (DNNs) in classic ensemble learning frameworks.
EDL can overcome challenges associated with uneven class distribution, small sample size, noisy data, etc.
EDL techniques are more robust than individual deep learning (DL) models and directly measure uncertainty by highlighting discrepancies between base models.
It can also improve generalization performance, reduce class bias, and detect nonlinear relationships in the data. Additionally, EDL methods are dynamic and can be easily updated with additional information.
Application of EDL law in case of AD
The classification and insights of AD-based EDL methods are based on each model's data access approach. In other words, it is slice-based or voxel-based. Slice-based approaches involve models using a two-dimensional (2D) input data approach rather than the entire 3D MRI scan.
On the other hand, in voxel-based approaches, the entire 3D neuroimage is adopted directly or from a 3D scan.
For AD detection with a slice-based approach, a homogeneous EDL approach, a heterogeneous EDL approach, or a stacking EDL approach can be used. For voxel-based methods, either a homogeneous EDL approach or a stacking EDL approach is used.
Additionally, unimodal and multimodal methodologies are considered for each approach. There can be added complexity when modeling neuroimaging data. In such situations, slice-based approaches that can handle 2D neuroscans are preferred over voxel-based approaches.
Integrating VGG-16-based models into a heterogeneous framework could lead to efficient AD detection. Focusing on learning has the potential to reduce computational constraints while preserving performance metrics.
The researchers also trained a convolutional neural network (CNN) algorithm on various 2D MRI slices to create an optimal and robust classifier ensemble.
Improved classification accuracy was achieved using a variety of data sources, including MRI, PET scans, and genetic markers. Genomic biomarker prediction was performed by combining genetic insights and neuroimaging data.
To ensure convergence of classification errors, homogeneous ensembles use many classifiers. For this reason, classifiers require large amounts of memory, and inference consumes significant computational power for each test case.
Heterogeneous ensembles extract the benefits of different base models to reveal unique characteristics of the training data. This improves generalization performance over homogeneous ensembles.
However, when developing a heterogeneous ensemble, the selection of complementary and diverse base models, the identification and selection of the optimal subset of classifiers, and the determination of the optimal set of weights must be carefully performed.
Overall, this review suggests having an efficient multimodal longitudinal approach as the ultimate goal for EDL-dependent AD prediction systems.
EDL can address common issues related to lack of data, the possibility of data siloing, or the presence of class imbalance.
Room for further development of EDL
Current research focuses on integrating medical knowledge-based features and behavioral variables to detect AD. More accurate detection frameworks may be developed to detect clinically homogeneous Alzheimer's disease patients or groups.
Using ML to integrate various biomarkers, medically informed features, neuropsychological tests, and brain imaging has the potential to significantly enhance Alzheimer's disease research and diagnosis.
Due to the high computational cost required to train an ensemble of independent models, it may not be practical to apply computationally expensive and complex EDL models to diagnose AD.
This is especially true when the datasets involved are large or when the individual models are large and deep architectures. Therefore, designing appropriate EDL-based architectures to overcome AD detection problems is a fruitful area for future research.
Another potential area for further development is to better incorporate new data modalities into the characterization of AD via EDL.
Beyond neuroimaging and traditional clinical assessments, it is becoming increasingly important to integrate diverse data types such as omics data and neuroimaging biomarkers.
These provide important insights into the underlying mechanisms and disease progression. However, potential challenges regarding computational cost, availability of robust analytical frameworks, and data quality still remain.
conclusion
This means that computer-based diagnostic approaches and clinical expertise can be effectively used to identify AD.
Ensemble DL technology has become very popular due to its ability to incorporate diverse data modalities. Also, their good generalization ability shows a remarkable improvement compared to previous AD diagnostic methods.
Reference magazines:
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Tanveer, M., Goel, T., Sharma, R., Malik, AK, Beheshti, I., Del Ser, J., Suganthan, PN, & Lin, CT (2024) For characterization and estimation of Alzheimer's disease. ensemble deep learning. natural mental health. 1-13. Doi: https://doi.org/10.1038/s44220-024-00237-x. https://www.nature.com/articles/s44220-024-00237-x