This review focuses on the recent advancements in neuroimaging enabled by deep learning techniques, specifically highlighting their applications in brain disorder detection and diagnosis. The integration of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has significantly improved feature extraction, pattern recognition, and predictive modeling, leading to enhanced accuracy, sensitivity, and specificity in diagnosing Alzheimer’s, Parkinson’s, schizophrenia, and brain tumors across MRI, fMRI, and PET scans. Despite these advancements, current challenges persist, including limitations in interpretability, data scarcity, and ethical concerns. To address these issues, future perspectives involve leveraging transfer learning, federated learning, and multimodal data sources. This review aims to provide a comprehensive overview of the current state of deep learning in neuroimaging, highlight the existing challenges, and discuss potential solutions for future innovation. By addressing the current limitations and exploring innovative techniques, deep learning can unlock new possibilities in neuroimaging, ultimately leading to improved diagnosis, treatment, and patient outcomes.
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