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Lung diseases pose significant challenges to public health worldwide, necessitating accurate and efficient diagnostic tools for effective management and treatment. This comprehensive review explores multi-modal image fusion, which combines information from multiple modalities like CT and CXR to create composite images with complementary data. This paper aims to study and analyze various fusion mechanisms used in deep learning algorithms to improve classification results. This paper also sheds light on hybrid techniques combined with deep learning. The review also identifies key research gaps and proposes potential avenues for future research, including integrating multi-modal data and developing robust models for real-time diagnosis.

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Exploring Deep Learning-Based Multi-modality Fusion Approaches in Classification of Lung Diseases: A Review

  • 会议论文

  • 发表日期：

    2025年

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  • Gautami Shingan,
  • Priya Ranjan

摘要

Lung diseases pose significant challenges to public health worldwide, necessitating accurate and efficient diagnostic tools for effective management and treatment. This comprehensive review explores multi-modal image fusion, which combines information from multiple modalities like CT and CXR to create composite images with complementary data. This paper aims to study and analyze various fusion mechanisms used in deep learning algorithms to improve classification results. This paper also sheds light on hybrid techniques combined with deep learning. The review also identifies key research gaps and proposes potential avenues for future research, including integrating multi-modal data and developing robust models for real-time diagnosis.