Alzheimer's disease is a neurodegenerative disease that affects brain tissues, causing memory loss. The most common and primary symptoms of Alzheimer's are the complexity of remembering more information, behavioral changes, and a deep confusion about events, locations, and time. The detection of Alzheimer's disease is desirable for therapy and patient care. Still, it is difficult to handle heterogeneous and high-dimensional biomedical data for attaining a reliable prediction. To solve these complexities, this paper devises the Multi-Axis-Multi-Head Capsule Network (MaxMHCapsuleNet)-based detection and classification of Alzheimer's disease. Initially, image sharpening is performed using a Kriging-Weighted Laplacian kernel, and the Spatial-Channel Mamba-UNet (SCM-UNet) segments the affected region. Furthermore, the techniques, such as 90° rotation and random masking, augment the images. In the feature extraction process, GoogLeNet and Visual Geometry Group Network (VGGNet) features are extracted. The Alzheimer's disease detection is done by MaxMHCapsuleNet, which detects the normal (non-demented) and abnormal classes. If the detected result is abnormal, multiclass classification is done by MaxMHCapsuleNet. In the classification process, the abnormal classes are classified into mild, moderate, and very mild demented stages. Moreover, the devised MaxMHCapsuleNet attains the accuracy, precision, and recall of 91.709%, 90.388%, and 92.036%.
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