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<p>Alzheimer’s Disease (AD) is a chronic neurodegenerative condition characterized by memory loss, affecting over millions of individuals worldwide and ranking as the seventh leading cause of global mortality as per World Health Organization. Early and precise diagnosis significantly improves the prognosis for AD patients. Various diagnostic markers, including neuroimaging, cerebrospinal fluid proteins, blood and urine tests, and genetic risk analysis, have been approved for AD detection. Deep Learning (DL) models, particularly utilizing neuroimaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) have been developed to automate AD diagnosis. The effectiveness of these methods depends on factors such as the nature of the problem, utilized datasets, and available resources. However, there exists a gap in comprehensive reviews that synthesize recent studies on AD analysis. This paper presents a thorough examination of employing diverse DL frameworks for predicting AD using neuroimaging. The primary goal is to identify optimal imaging modalities and DL strategies for handling extensive datasets and delivering precise AD predictions. The results of this review reveal that most studies employ Convolutional Neural Networks (CNNs) to construct effective AD classification models. In contrast, others utilize a combination of Hybrid DL architectures, including Deep Convolutional Neural Networks (DCNNs) and pre-trained models. Furthermore, the review addresses ten significant challenges that future researchers may encounter in this domain. Lastly, this study offers a detailed analysis of DL-based AD detection, classification, and segmentation across various imaging modalities.</p>

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A Comprehensive Review of Deep Learning Methodologies for Alzheimer’s Disease Diagnosis by Neuroimaging

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Dontha Madhusudhana Rao,
  • Saroj Kumar Biswas,
  • Monali Bordoloi

摘要

Alzheimer’s Disease (AD) is a chronic neurodegenerative condition characterized by memory loss, affecting over millions of individuals worldwide and ranking as the seventh leading cause of global mortality as per World Health Organization. Early and precise diagnosis significantly improves the prognosis for AD patients. Various diagnostic markers, including neuroimaging, cerebrospinal fluid proteins, blood and urine tests, and genetic risk analysis, have been approved for AD detection. Deep Learning (DL) models, particularly utilizing neuroimaging modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) have been developed to automate AD diagnosis. The effectiveness of these methods depends on factors such as the nature of the problem, utilized datasets, and available resources. However, there exists a gap in comprehensive reviews that synthesize recent studies on AD analysis. This paper presents a thorough examination of employing diverse DL frameworks for predicting AD using neuroimaging. The primary goal is to identify optimal imaging modalities and DL strategies for handling extensive datasets and delivering precise AD predictions. The results of this review reveal that most studies employ Convolutional Neural Networks (CNNs) to construct effective AD classification models. In contrast, others utilize a combination of Hybrid DL architectures, including Deep Convolutional Neural Networks (DCNNs) and pre-trained models. Furthermore, the review addresses ten significant challenges that future researchers may encounter in this domain. Lastly, this study offers a detailed analysis of DL-based AD detection, classification, and segmentation across various imaging modalities.