Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review
Abstract
:1. Introduction
2. Transformation from ML to DL Approaches for the Effective Prediction of AD
- “Free Surfer” is an application for cerebral localization with cortex-associated information.
- The “SPM5 (Statistical Parametric Mapping Tool)” is a device for the mapping of statistical parameters.
3. Diagnosis and Prognosis of AD Using DL Methods
3.1. Gradient Computation
3.2. DNNs in the Real World
3.3. DNN Architectures
3.4. DL for Selection of Attributes from Neuroimaging Information
3.5. DL for Selection of Heterogeneous Neuroimaging Data
4. Case Studies on the Diagnosis of AD Using DL and Related Technologies
- During the last 15 years, the utilization of such techniques and AI technologies in medical applications has skyrocketed. There seem to be three important aspects to consider, e.g., information quantity and quality have both improved. In this sense, the discipline is approaching Big Data.
- In detection with the help of computers (CADe), certain components in the imagery, such as structures or neurons, can be identified. CADe can also be used to identify areas of focus for scientists, like malignancies.
- Segmentation is the separation of complete picture portions from the rest of the imaging.
- Computer-aided Diagnosis (CADx) denotes a diagnostic based on particular data that can be described as a categorization task in plain terms. Medical photos are employed in this scenario, which emphasizes the necessity of CNN. In the context of AD, there are three classifications: NC, MCI, and AD.
5. Research Challenges in DL for AD
6. Discussion
7. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Reference | DL Technique | Accuracy |
---|---|---|
[69] | SAEsoftmax” regression layer | >86% |
[70] | 3D-CNN | >87% |
[72] | SAE SoftMax” regression layer | >90% |
[73] | SAE DNN | >84% for AD/CN classification >82% for MCI to AD classification |
[74] | 3D CNN | >92% for AD/CN classification >72% for MCI to AD conversion |
[75] | VoxCNN ResNet | >79% |
[77] | 2D CNN | >85% |
[78] | 3D CNN | >75% for MCI to AD conversion |
[80] | SAE 3D CNN | >90% |
[81] | Ensemble of 2D CNN and RNN | >91% |
[83] | 3D CNN | >95% for AD/CN classification >84% for MCI to AD conversion |
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Shastry, K.A.; Vijayakumar, V.; V, M.K.M.; B A, M.; B N, C. Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review. Healthcare 2022, 10, 1842. https://doi.org/10.3390/healthcare10101842
Shastry KA, Vijayakumar V, V MKM, B A M, B N C. Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review. Healthcare. 2022; 10(10):1842. https://doi.org/10.3390/healthcare10101842
Chicago/Turabian StyleShastry, K Aditya, V Vijayakumar, Manoj Kumar M V, Manjunatha B A, and Chandrashekhar B N. 2022. "Deep Learning Techniques for the Effective Prediction of Alzheimer’s Disease: A Comprehensive Review" Healthcare 10, no. 10: 1842. https://doi.org/10.3390/healthcare10101842