Detection of Alzheimer’s Disease with Shape Analysis of MRI Images - A Glance

Alzheimer’s disease (AD) is a progressive neurode- generative disorder and the leading cause of dementia worldwide, making early and accurate diagnosis a critical clinical challenge. In recent years, artificial intelligence (AI)–based techniques have gained significant attention for assisting clinicians in the auto- mated detection and staging of Alzheimer’s disease. Numerous studies have explored deep learning approaches, particularly Convolutional Neural Networks (CNNs), for analyzing brain magnetic resonance imaging (MRI) data, as well as alternative biomarkers such as eye-movement patterns for early cognitive decline detection. Parallel advancements in transfer learning, hyperparameter optimization, and lightweight neural architec- tures have further improved diagnostic accuracy while reducing computational complexity. This literature survey reviews and analyzes key research contributions in AI-driven Alzheimer’s disease detection, focusing on MRI-based deep learning models, optimization strategies, multiclass disease classification, and non-invasive eye-tracking techniques. The surveyed works are examined in terms of datasets used, learning architectures, evaluation metrics, and reported performance. While many studies demonstrate high accuracy and promising clinical applicability, challenges remain regarding dataset imbalance, model generalization, interpretabil- ity, and real-world deployment. The survey highlights emerging trends such as explainable AI, mobile-based screening tools, and optimized deep learning frameworks as potential solutions to these limitations. Overall, this study emphasizes the need for robust, interpretable, and scalable AI-assisted diagnostic systems to support early Alzheimer’s detection and improve clinical decision-making..