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This study delves into the pivotal role of deep learning algorithms in advancing the early diagnosis of Alzheimer's disease (AD) through medical image processing. Analyzing research articles from diverse databases, the review rigorously examines deep learning techniques, specifically focusing on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning (TL). The inclusion criteria ensure the incorporation of the most relevant findings in AD detection. Emphasizing neuroimaging modalities such as Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), the research underscores the significance of a comprehensive analysis of radiological features to enhance the precision of AD detection, segmentation, and severity grading. The review deliberately excludes studies utilizing alternative biomarkers, prioritizing works grounded in radiological imaging data for AD detection and strictly considering English-published articles. The study concludes by highlighting the imperative for a deeper exploration of the progression from Mild Cognitive Impairment (MCI) to AD using advanced deep learning models, identifying crucial research challenges in the realm of AD diagnosis, and emphasizing the ongoing pursuit of heightened diagnostic accuracy.

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A Multidimensional Journey Through Neuroimaging and Advanced Machine Learning for Alzheimer’s Disease Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Hardeep Kaur,
  • Anil Kumar,
  • Varinder Kaur Attri

摘要

This study delves into the pivotal role of deep learning algorithms in advancing the early diagnosis of Alzheimer's disease (AD) through medical image processing. Analyzing research articles from diverse databases, the review rigorously examines deep learning techniques, specifically focusing on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning (TL). The inclusion criteria ensure the incorporation of the most relevant findings in AD detection. Emphasizing neuroimaging modalities such as Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), the research underscores the significance of a comprehensive analysis of radiological features to enhance the precision of AD detection, segmentation, and severity grading. The review deliberately excludes studies utilizing alternative biomarkers, prioritizing works grounded in radiological imaging data for AD detection and strictly considering English-published articles. The study concludes by highlighting the imperative for a deeper exploration of the progression from Mild Cognitive Impairment (MCI) to AD using advanced deep learning models, identifying crucial research challenges in the realm of AD diagnosis, and emphasizing the ongoing pursuit of heightened diagnostic accuracy.