Detection of different progression stages of Alzheimer’s disease using SURF

The proposed model that utilizes speeded-up robust features (SURF) and multi-layer perceptron (MLP) for the classification of the four stages of dementia is a promising development. The four stages of dementia are cognitively normal, Alzheimer’s disease, mild cognitive impairment convertible, mild cognitive impairment non-convertible. Accurate classification of these stages is crucial for early detection and intervention of Alzheimer’s disease, as early treatment can slow down memory loss. The use of SURF, a widely used feature descriptor in computer vision, for Alzheimer’s disease detection is innovative and shows potential. The MLP, which is commonly used for classification tasks, is also suitable for this problem. However, it is important to validate the model’s performance on larger datasets to ensure its reliability and effectiveness. This study demonstrates the potential of machine learning and computer vision techniques in assisting with the diagnosis and management of Alzheimer’s disease.