Ensemble deep learning models enhance early diagnosis of Alzheimer’s disease using neuroimaging data

A recent Nature Mental Health study assessed the developments in 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 several machine learning (ML) models to enhance their generalization performance. The traditional approach to building an ensemble uses deep neural networks (DNNs) in a classical ensemble learning framework.

EDL can overcome challenges related to unequal class distributions, small sample sizes, noisy data, etc.

EDL methods are more robust than individual deep learning (DL) models and measure uncertainty directly by highlighting the disagreement between base models.

They also improve generalization performance, reduce class bias, and can detect non-linear relationships in data. Furthermore, EDL methods are dynamic and can be updated easily with additional information.

Application of EDL methods in case of AD

The categorization of and insights into AD-based EDL methods is based on each model’s data-accessing approach. In other words, this is slice-based or voxel-based. Slice-based approaches concern models with a two-dimensional (2D) input data approach instead of an entire 3D MRI scan.

On the other hand, in Voxel-based approaches, the entire 3D neuroimage is adopted directly or from 3D scans. 

For AD detection via 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.

Furthermore, for each of the approaches, single- and multi-modal methodologies have been considered.  When modeling neuroimaging data, the complexity could increase. In these situations, slice-based approaches are preferred to voxel-based approaches, as they can handle 2D neuroscans.

Integrating VGG-16-based models in a heterogeneous framework could lead to efficient AD detection. The emphasis on learning could mitigate computational constraints while maintaining performance metrics.

Researchers have also trained convolutional neural network (CNN) algorithms over different 2D MRI slices, which created optimal and robust classifier ensembles.

Enhanced classification accuracy has been achieved using varied data sources, such as MRI and PET scans and genetic markers. The prediction of genome biomarkers was conducted by combining genetic insights and neuroimaging data.

To ensure convergence of classification error a homogeneous ensemble makes use of many classifiers. Due to this reason, classifiers require a large amount of memory, and inference consumes substantial computing power for every test case.

Heterogeneous ensembles extract the upsides of varied base models to uncover distinctive properties of the training data. This offers more generalization performance than homogeneous ensembles.

However, while developing heterogeneous ensembles, the selection of complementary and diverse base models, the identification and selection of an optimal subset of classifiers, and the determination of an optimal set of weights should be carefully performed.

Overall, this review suggests having an efficient multimodal longitudinal method as the final goal for an AD prediction system depending on EDL.

EDL is capable of dealing with common issues concerning the scarcity of data, the potential of data being siloed, or the presence of class imbalance. 

Scope for further development of EDL

The current research focuses on integrating medical knowledge-based features and behavioral variables to detect AD. More accurate detection frameworks could be developed to detect clinically homogeneous individuals or groups with AD.

The use of ML to bring together different biomarkers, medical knowledge-based features, neuropsychological tests, and brain imaging could significantly enhance AD research and diagnosis.

The application of computationally expensive complex EDL models may not be feasible to diagnose AD because the amount of computing required to train an ensemble of independent models is costly.

This is especially true if the datasets involved are large or if individual models are large, deep architectures. Therefore, designing appropriate EDL-based architectures to overcome the problems with AD detection is a fruitful area for future research.

Another potential area for further development could be better incorporating new data modalities into AD characterization 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 offer key insights into the underlying mechanisms and disease progression. However, potential challenges around computational costs, availability of robust analytical frameworks, and data quality remain. 

Conclusions

In sum, a computer-based diagnosis approach and clinical expertise could be used effectively to identify AD.

Ensemble DL techniques have gained immense popularity owing to their ability to incorporate diverse data modalities. Their superior generalization capabilities also represent a marked improvement over previous methods of diagnosing AD.