A spatial correlation-guided deep fusion framework for multimodal lung cancer classification using CT imaging

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Lung cancer is a leading cause of death globally, necessitating accurate diagnostic methods for improved survival rates.
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The proposed deep learning framework utilizes Spatial Correlation Mapping to enhance multimodal lung cancer classification.
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The model achieves 98% accuracy and 100% recall on malignant tumors, outperforming traditional single-modality approaches.
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A correlation-guided fusion strategy allows for effective integration of heterogeneous features while maintaining spatial coherence.
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The framework demonstrates computational efficiency, making it suitable for real-world clinical applications in lung cancer diagnosis.

Frontiers In Medicine

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