TinyVit-LightGBM: A lightweight and smart feature fusion framework for IoMT-based cancer diagnosis

Cancer remains a leading global health issue, where accurate and timely diagnosis is critical for effective treatment. The Internet of Medical Things (IoMT), an interconnected network of medical devices, offers real-time multimodal and multi-source data acquisition and analysis, facilitating remote monitoring and improving diagnostic precision. However, IoMT-based diagnostic frameworks face major challenges, including limited computational resources of IoMT devices, difficulties in integrating multimodal data from diverse sources, and the necessity for interpretable models to enhance clinical trust. To address these issues, we propose TinyViT-LightGBM, a lightweight and smart multimodal data fusion framework optimized for breast cancer diagnostics in resource-constrained IoMT environments. TinyViT, an efficient Vision Transformer, extracts features from multi-source histopathology images, combined with mammograms and clinical-genetic data through a comprehensive fusion strategy. By using LightGBM for classification, the framework not only achieves high diagnostic accuracy but also enhances interpretability by identifying the most critical diagnostic features. The proposed framework achieves state-of-the-art diagnostic performance, with 97.8% accuracy, a 6.5% improvement over existing methods, alongside gains in precision (97.2%), recall (99.1%), and F1-score (98.1%). Additionally, its low false positive rate (0.0058) and computational efficiency on IoMT devices underscore its scalability and suitability for real-world healthcare applications. • TinyViT-LightGBM is a novel multimodal data fusion framework for IoMT-based breast cancer diagnostics. • The model addresses the issues of computational constraints, multi source data, and data interpretability. • The LightGBM classify the vital feature for accurate decision-making. • The model achieves better diagnostic results with 97.8% accuracy, 97.2% precision, 99.1% recall, and 98.1% F1-score.