Hybrid deep learning framework MedFusionNet assists multilabel biomedical risk stratification from imaging and tabular data.

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Abstract

Multi-label medical image classification is challenging due to complex inter-label dependencies, data imbalance, and the need to integrate multiple data modalities. These challenges hinder the development of robust and interpretable diagnostic systems capable of leveraging diverse clinical information. We propose a cancer risk stratification framework that combines univariate thresholding with multivariate modeling using a hybrid parallel deep learning architecture, MedFusionNet. First, univariate thresholds are applied to identify the top-N discriminative features for each label. These selected features are then incorporated into MedFusionNet, which integrates Self-Attention Mechanisms, Dense Connections, and Feature Pyramid Networks (FPNs). The architecture is further extended for multi-modal learning by fusing image data with corresponding textual and clinical metadata. Self-Attention captures dependencies across image regions, labels, and modalities; Dense Connections enable efficient feature propagation; and FPNs support multi-scale representation and cross-modal fusion. Extensive evaluations on multiple datasets, including NIH ChestX-ray14 and a custom cervical cancer dataset, confirm that MedFusionNet consistently outperforms existing models. The framework delivers higher accuracy, improved robustness, and enhanced interpretability compared to traditional deep learning approaches. MedFusionNet provides an effective and scalable solution for multi-label medical image classification and cancer risk stratification. By integrating multi-modal information and advanced architectural components, it improves predictive performance while maintaining high interpretability, making it well-suited for real-world clinical applications.

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