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Deep Learning Approaches for Alzheimer’s Disease Diagnosis: A Comprehensive Review

Authors :

Mahdi Jafari Asl¹ Saba Haji Molla Rabie²

1- دانشگاه شاهد-تهران 2- دانشگاه ازاد تهران جنوب

Keywords :

Alzheimer’s disease (AD)،Mild Cognitive Impairment (MCI)،Electroencephalography (EEG)،Magnetic Resonance Imaging (MRI)،Deep Learning (DL)،Convolutional Neural Networks (CNNs)،Recurrent Neural Networks (RNN)،Transformers،Multimodal analysis

Abstract :

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that severely affects memory, cognition, and daily functioning. Early and reliable diagnosis plays a critical role in improving treatment strategies and patient quality of life. In recent years, deep learning (DL) techniques have shown remarkable potential in medical imaging and neurophysiological signal analysis, enabling automated and accurate detection of AD and related conditions such as mild cognitive impairment (MCI) and frontotemporal dementia (FTD). This paper provides a comprehensive review of 17 recent studies published between 2023 and 2025, which applied various DL architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and hybrid models, to electroencephalography (EEG), magnetic resonance imaging (MRI), and multimodal datasets. The reviewed works adopted diverse feature extraction strategies such as discrete wavelet transform (DWT), synchrosqueezing, power spectral density (PSD), and intrinsic time-scale decomposition (ITD), combined with advanced classifiers and interpretable AI frameworks. A comparative analysis highlights the strengths and limitations of each approach, dataset availability, and diagnostic accuracy. Finally, key challenges, including limited dataset diversity, lack of interpretability, and generalization issues, are discussed, along with future directions such as explainable AI, federated learning, and multimodal fusion, which hold promise for advancing AD diagnosis.

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