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<p>Early and accurate diagnosis of Alzheimer’s disease (AD) is crucial for effective treatment. While the integration of deep learning techniques for AD classification is not entirely new, this study introduces CAPCBAM—a framework that extends prior approaches by combining Capsule Networks with a Convolutional Block Attention Module (CBAM). In CAPCBAM, standardized preprocessing of MRI images is followed by feature extraction using Capsule Networks, which preserve spatial hierarchies and capture intricate relationships among image features. The subsequent application of CBAM, employing both channel and spatial attention mechanisms, refines the feature maps to highlight the most clinically relevant regions. This dual-attention strategy offers clear advantages over conventional CNN methods, particularly in enhancing model generalization and mitigating information loss due to pooling. On the ADNI dataset, CAPCBAM achieved an impressive accuracy of 99.95%, with precision and recall both at 99.8%, an AUC of 0.99, and an F1-Score of 99.92%. Although the use of Capsule Networks and attention mechanisms has been explored previously, CAPCBAM distinguishes itself by its robust integration of these components. The study’s advantages include improved feature extraction, faster convergence, and superior classification performance, making it a promising tool for the early detection of Alzheimer’s disease.</p>

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Revolutionizing Alzheimer’s disease detection with a cutting-edge CAPCBAM deep learning framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月22日

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  • Houmem Slimi,
  • Sabeur Abid,
  • Mounir Sayadi

摘要

Early and accurate diagnosis of Alzheimer’s disease (AD) is crucial for effective treatment. While the integration of deep learning techniques for AD classification is not entirely new, this study introduces CAPCBAM—a framework that extends prior approaches by combining Capsule Networks with a Convolutional Block Attention Module (CBAM). In CAPCBAM, standardized preprocessing of MRI images is followed by feature extraction using Capsule Networks, which preserve spatial hierarchies and capture intricate relationships among image features. The subsequent application of CBAM, employing both channel and spatial attention mechanisms, refines the feature maps to highlight the most clinically relevant regions. This dual-attention strategy offers clear advantages over conventional CNN methods, particularly in enhancing model generalization and mitigating information loss due to pooling. On the ADNI dataset, CAPCBAM achieved an impressive accuracy of 99.95%, with precision and recall both at 99.8%, an AUC of 0.99, and an F1-Score of 99.92%. Although the use of Capsule Networks and attention mechanisms has been explored previously, CAPCBAM distinguishes itself by its robust integration of these components. The study’s advantages include improved feature extraction, faster convergence, and superior classification performance, making it a promising tool for the early detection of Alzheimer’s disease.