Alzheimer’s disease (AD) is a progressive neurological problem that causes brain atrophy and affects the memory and thinking skills of an individual. Accurate detection of AD has been a challenging research topic for a long time in the area of medical image processing. Detecting AD at its earliest stage is crucial for the successful treatment of the disease. The proposed Adaptive Hybrid Attention Network (AHANet) has two attention modules, namely Enhanced Non-Local Attention (ENLA) and Coordinate Attention. These modules extract global-level features and local-level features separately from the brain Magnetic Resonance Imaging (MRI), thereby boosting the feature extraction power of the network. The ENLA module extracts spatial and contextual information on a global scale while also capturing important long-range dependencies. The Coordinate Attention module captures local features from the input images. It embeds positional information into the channel attention mechanism for enhanced feature extraction. Moreover, an Adaptive Feature Aggregation (AFA) module is proposed to fuse features from the global and local levels in an effective way. As a result of incorporating the above architectural enhancements into the DenseNet architecture, the proposed network exhibited better performance compared to the existing works. The proposed network was trained and tested on the ADNI dataset, yielding a classification accuracy of 98.53%.

Alzheimer's disease (AD) is an irreversible and degenerative brain condition that gradually damages memory and thinking abilities. Despite being incurable, AD causes significant pain and financial hardship to patients and their families. However, medications are most effective when administered early in the course of the disease and early diagnosis is crucial in the treatment of AD to restrict its progression. There are several approaches proposed for computer-assisted AD diagnosis that involve structural and functional imaging modalities, such as sMRI, fMRI, DTI, and PET. Machine learning and deep learning techniques have facilitated the development of novel models for diagnostic accuracy in AD. This research compares the performance of several machine learning and deep convolutional architectures to detect AD from MCI. It is essential to find the effective baseline model for classifying AD, hence all the pre-trained models are evaluated with benchmark dataset. Experimental observations indicate that the DenseNet-169 performed best out of different state-of-the-art architectures, with an average accuracy of 82.2%.