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<p>Asthma and chronic obstructive pulmonary disease (COPD) are prevalent lung illnesses requiring accurate classification for effective treatment. Conventional diagnostic methods, though reliable, are often invasive, costly, and require specialized expertise. Recent advancements in machine learning (ML) have enhanced COPD classification using clinical, imaging, and physiological data. This study introduces SAHPN, an ensemble deep learning model that integrates a Fusion Depthwise-Separable Block (FDSB) and a Deep Residual Feature Distillation Block (DRFDB) to optimize feature extraction while reducing memory and computational demands. The classifier employs multiple convolutional branches with varied filter sizes and kernels to capture diverse feature representations. Spatial attention units refine learning by focusing on relevant details, while second-order pooling models high-level feature interactions. A concatenation fusion technique merges outputs into a multimodal representation for improved classification. To further optimize feature extraction and classification, we incorporate the Attack-Leave Optimizer (ALO), which balances guided and random searches for enhanced accuracy. The SAHPN model is evaluated on 7,194 contrast-enhanced CT (CECT) images from 78 participants (3,597 COPD and 3,597 healthy controls), while its generalizability to non-contrast CT (NCCT) images is addressed separately. Results demonstrate that SAHPN outperforms existing models in classification accuracy. The proposed model is novel in its integration of spatial attention, high-order pooling, and the Attack-Leave Optimizer within an ensemble framework, providing a robust, efficient, and scalable approach for COPD diagnosis from contrast-enhanced CT images.</p>

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A novel ensemble deep learning framework with spatial attention and high-order pooling for COPD detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月18日

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  • Srikanth Cherukuvada,
  • R. Krishna Chaitanya,
  • M. Janardhan,
  • Srinivas Yara,
  • S. K. Khaja Shareef,
  • M. Harshini,
  • Raviteja Kocherla

摘要

Asthma and chronic obstructive pulmonary disease (COPD) are prevalent lung illnesses requiring accurate classification for effective treatment. Conventional diagnostic methods, though reliable, are often invasive, costly, and require specialized expertise. Recent advancements in machine learning (ML) have enhanced COPD classification using clinical, imaging, and physiological data. This study introduces SAHPN, an ensemble deep learning model that integrates a Fusion Depthwise-Separable Block (FDSB) and a Deep Residual Feature Distillation Block (DRFDB) to optimize feature extraction while reducing memory and computational demands. The classifier employs multiple convolutional branches with varied filter sizes and kernels to capture diverse feature representations. Spatial attention units refine learning by focusing on relevant details, while second-order pooling models high-level feature interactions. A concatenation fusion technique merges outputs into a multimodal representation for improved classification. To further optimize feature extraction and classification, we incorporate the Attack-Leave Optimizer (ALO), which balances guided and random searches for enhanced accuracy. The SAHPN model is evaluated on 7,194 contrast-enhanced CT (CECT) images from 78 participants (3,597 COPD and 3,597 healthy controls), while its generalizability to non-contrast CT (NCCT) images is addressed separately. Results demonstrate that SAHPN outperforms existing models in classification accuracy. The proposed model is novel in its integration of spatial attention, high-order pooling, and the Attack-Leave Optimizer within an ensemble framework, providing a robust, efficient, and scalable approach for COPD diagnosis from contrast-enhanced CT images.