Multi-scale Attention Network for Early Detection of Alzheimer's Disease from MRI images

Alzheimer's disease (AD) is a chronic neurodegen-erative disorder that affects brain cells and causes irreversible memory loss, often known as dementia. Many individuals die from this disease each year due to its incurable nature. However, the timely identification of the ailment can play a pivotal role in mitigating its progression. Nowadays, deep learning is used to design an automated system that can detect and classify AD in the early stages. Thus, a novel multi-scale attention network (MSAN-Net) is introduced in this study. The proposed technique uses brain magnetic resonance imaging (MRI) to categorize images into four stages; non-demented, mild demented, very mild demented, and moderate demented. The proposed work is compared with four state-of-the-art methods, and the experimental results suggest that the MSAN-Net exhibits superior performance than the compared approaches.