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Open Access | Just Accepted

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

Mohamadreza Khosravi1( )Hossein Parsaei1,2( )Khosro Rezaee3

1 Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Fars 71348-45794, Iran

2 Shiraz Neuroscience Research Center, Shiraz University of Medical Sciences, Shiraz, Fars 71348-45794, Iran

3 Department of Biomedical Engineering, Meybod University, Meybod, Yazd 89616-99557, Iran

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Abstract

In neuropathological diseases such as Alzheimer’s disease (AD), neuroimaging and magnetic resonance imaging (MRI) play a crucial role 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 intraclass 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 twostream architecture. Additionally, two new cost functions, Satisfied Rank loss and Cross-network Similarity loss, 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.

Tsinghua Science and Technology
Cite this article:
Khosravi M, Parsaei H, Rezaee K. 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. Tsinghua Science and Technology, 2024, https://doi.org/10.26599/TST.2024.9010080

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Received: 26 December 2023
Revised: 19 April 2024
Accepted: 24 April 2024
Available online: 03 June 2024

© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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