Novel Classification Scheme for Early Alzheimer's Disease (AD) Severity Diagnosis Using Deep Features of the Hybrid Cascade Attention Architecture: Early Detection of AD on MRI Scans

In neuropathological diseases such as Alzheimer's Disease (AD), neuroimaging and Magnetic Resonance Imaging (MRI) play crucial roles in the realm of Artificial Intelligence of Medical Things (AIoMT) by leveraging edge intelligence resources. However, accurately classifying MRI scans based on neurodegenerative diseases faces challenges due to significant variability across classes and limited intra-class differences. To address this challenge, we propose a novel approach aimed at improving the early detection of AD through MRI imaging. This method integrates a Convolutional Neural Network (CNN) with a Cascade Attention Model (CAM-CNN). The CAM-CNN model outperforms traditional CNNs in AD classification accuracy and processing complexity. In this architecture, the attention mechanism is effectively implemented by utilizing two constraint cost functions and a cross-network with diverse pre-trained parameters for a two-stream architecture. Additionally, two new cost functions, Satisfied Rank Loss (SRL) and Cross-Network Similarity Loss (CNSL), are introduced to enhance collaboration and overall network performance. Finally, a unique entropy addition method is employed in the attention module for network integration, converting intermediate outcomes into the final prediction. These components are designed to work collaboratively and can be sequentially trained for optimal performance, thereby enhancing the effectiveness of AD stage classification and robustness to interference from MR images. Validation using the Kaggle dataset demonstrates the model's accuracy of 99.07% in multiclass classification, ensuring precise classification and early detection of all AD subtypes. Further validation across three feature categories with varying numbers confirms the robustness of the proposed approach, with deviations from the standard criteria of less than 1%. Applied in Alzheimer's patient care, this capability holds promise for enhancing value-based therapy and clinical decision-making. It aids in differentiating Alzheimer's patients from healthy individuals, thereby improving patient care and enabling more targeted therapies.