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<p>The primary focus of this research article is to advance convolutional neural networks (CNNs) through the incorporation of Squeeze and Excitation (SE) and Convolutional Block Attention Module (CBAM) blocks, with an emphasis on recalibrating channel wise features. This approach highlights channel relationships, with the SE block seamlessly integrated into a pre-trained DenseNet CNN model to recalibrate channel wise feature responses by explicitly modeling channel interdependencies. The study demonstrates the construction of SENet and CBAM architectures with SE and CBAM blocks on pretrained DenseNet models using transfer learning, achieving exceptional generalization on a cataract image dataset. The proposed architectures utilize layers of compressed network blocks that can establish direct connections with preceding layers through adjustable weight modifications. Comprehensive evaluations indicate that the DenseSE and DenseCBAM models both achieve 99% testing accuracy and exhibit strong performance across various metrics. Notably, DenseNet169 with SE and CBAM blocks attained 99% precision, surpassing the baseline model’s 95% precision. Furthermore, the Grad CAM technique enhances cataract disease identification, with the DenseCBAM and DenseSE models generating detailed heatmaps that support healthcare professionals in making swift and accurate retinal diagnoses.</p>

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Fusing Attention Mechanisms: Unleashing DenseSE and DenseCBAM Synergy for Eye Disease Identification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月22日

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  • Deepak Kumar,
  • Brijesh Bakariya,
  • Chaman Verma

摘要

The primary focus of this research article is to advance convolutional neural networks (CNNs) through the incorporation of Squeeze and Excitation (SE) and Convolutional Block Attention Module (CBAM) blocks, with an emphasis on recalibrating channel wise features. This approach highlights channel relationships, with the SE block seamlessly integrated into a pre-trained DenseNet CNN model to recalibrate channel wise feature responses by explicitly modeling channel interdependencies. The study demonstrates the construction of SENet and CBAM architectures with SE and CBAM blocks on pretrained DenseNet models using transfer learning, achieving exceptional generalization on a cataract image dataset. The proposed architectures utilize layers of compressed network blocks that can establish direct connections with preceding layers through adjustable weight modifications. Comprehensive evaluations indicate that the DenseSE and DenseCBAM models both achieve 99% testing accuracy and exhibit strong performance across various metrics. Notably, DenseNet169 with SE and CBAM blocks attained 99% precision, surpassing the baseline model’s 95% precision. Furthermore, the Grad CAM technique enhances cataract disease identification, with the DenseCBAM and DenseSE models generating detailed heatmaps that support healthcare professionals in making swift and accurate retinal diagnoses.