Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches

TL;DR

This paper reviews recent deep learning methods applied to MRI images for early diagnosis and stage detection of Alzheimer's disease, highlighting advancements in pattern recognition for improved patient outcomes.

Contribution

It analyzes and compares five recent studies on MRI-based deep learning techniques for Alzheimer's diagnosis, providing an in-depth understanding of their differences and functionalities.

Findings

Deep learning enables early AD detection from MRI data.

Various models show high accuracy in stage classification.

The review highlights key methodological differences.

Abstract

The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis and stage detection are the most crucial elements in enhancing patient care and treatment outcomes, deep learning algorithms for MRI images have recently allowed for diagnosing a medical condition at the beginning stage and identifying particular symptoms of Alzheimer's disease. As a result, we aimed to analyze five specific studies focused on AD diagnosis using MRI-based deep learning algorithms between 2021 and 2023 in this study. To completely illustrate the differences between these techniques and comprehend how deep learning algorithms function, we attempted to explore selected approaches in depth.