Journal of Engineering Research and Reports

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects memory, reasoning, language, and other cognitive functions. Early identification is important because structural brain changes may occur before severe clinical symptoms become evident. Magnetic Resonance Imaging (MRI) provides detailed anatomical information and has therefore become an important source for computer-aided Alzheimer’s disease assessment. Artificial intelligence (AI) techniques have increasingly been applied to MRI-based classification, progressing from handcrafted features and conventional machine-learning methods to deep-learning approaches. Traditional methods use morphological, textural, statistical, and frequency-domain features with classifiers such as Support Vector Machine, Random Forest, and Artificial Neural Networks. Deep-learning models, including Convolutional Neural Networks (CNNs), three-dimensional CNNs, transfer-learning architectures, ResNet, DenseNet, and EfficientNet, can automatically learn disease-related image representations. Recent approaches, including Vision Transformers, Explainable Artificial Intelligence (XAI), multimodal learning, federated learning, and self-supervised learning, aim to improve global feature representation, interpretability, data utilisation, privacy, and generalisation. This review examines the development of AI techniques for MRI-based Alzheimer’s disease classification, including datasets, preprocessing, feature extraction, deep learning, and emerging methods. A comparative discussion highlights their strengths and limitations, while major challenges such as limited datasets, class imbalance, scanner variability, computational complexity, and insufficient external validation are discussed. Future research should focus on robust, explainable, multimodal, privacy-aware, and clinically validated AI systems.