Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future...

Area of Science:

• Medical Image Processing
• Artificial Intelligence in Healthcare
• Neuroimaging Analysis

Background:

• Deep learning (DL) algorithms are increasingly vital in medical image processing for disease diagnosis.
• Radiologists benefit from DL tools for accurate and efficient disease detection.
• Alzheimer's Disease (AD) diagnosis presents challenges addressable by advanced computational methods.

Purpose of the Study:

• To highlight the importance and application of deep learning models in Alzheimer's Disease detection.
• To analyze and review various deep learning methodologies employed for AD detection using neuroimaging data.
• To identify key research challenges and future directions for effective AD detection.

Main Methods:

• Systematic review of 103 research articles selected based on specific criteria.
• Analysis focused on deep learning techniques including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL).
• Consideration of neuroimaging modalities such as Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI).

Main Results:

• Deep learning models show significant promise in detecting Alzheimer's Disease from radiological imaging data.
• Various DL techniques have been applied for detection, segmentation, and severity grading of AD.
• The review identified promising results across different DL approaches for AD detection.

Conclusions:

• Deep learning offers powerful tools for enhancing Alzheimer's Disease detection and diagnosis.
• Further in-depth analysis using DL models is crucial for understanding the progression from Mild Cognitive Impairment (MCI) to AD.
• Addressing key research issues is essential for advancing DL-based AD detection.