A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer's Dementia Detection.

Area of Science:

• Neuroscience
• Medical Imaging
• Artificial Intelligence

Background:

• Alzheimer's Dementia (AD) is a progressive neurological disorder impacting memory and cognition, posing a significant global public health challenge.
• Early and accurate detection of dementia is crucial for timely intervention, treatment planning, and differentiating it from other conditions with similar symptoms.
• Advancements in detection methods drive medical research, aiding in biomarker identification, refining diagnostic tools, and developing innovative technologies for dementia diagnosis.

Purpose of the Study:

• To critically analyze multimodal imaging datasets, learning algorithms, and optimization techniques for Alzheimer's Dementia detection.
• To understand advancements and challenges in using diverse imaging modalities like MRI, PET, and EEG for dementia detection.
• To evaluate machine learning, deep learning, transfer learning, and generative adversarial networks for analyzing multi-modality imaging data in dementia detection.

Main Methods:

• Critical analysis of multimodal imaging datasets (MRI, PET, EEG).
• Evaluation of machine learning algorithms, deep learning models, transfer learning, and generative adversarial networks.
• Examination of optimization techniques, including optimization algorithms and hyperparameter tuning for image processing and analysis.

Main Results:

• The study provides a comprehensive overview of current methods for Alzheimer's Dementia detection using multimodal imaging and AI.
• It highlights the effectiveness of various machine learning and deep learning approaches in analyzing complex imaging data.
• The influence of optimization techniques on model performance and generalization in dementia detection is critically examined.

Conclusions:

• Enhancing dementia detection methods is fundamental to addressing healthcare challenges, enabling timely interventions, and improving diagnostic accuracy.
• Continued research and technological advancements in multimodal imaging analysis are essential for combating neurodegenerative diseases like AD.
• This work contributes to advancing diagnostic accuracy and research in neurodegenerative diseases through improved detection strategies.