Citation :

Bolleddu Devananda Rao, K. Madhavi, "BoneCancerFusionNet: A Multimodal Deep Learning Framework Integrating Medical Images and Clinical Data for Efficient Bone Cancer Detection and Classification," International Journal of Electronics and Communication Engineering, vol. 13, no. 4, pp. 94-108, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I4P107

Abstract

Bone cancer is a rare malignancy, with diagnosis presenting a major challenge, and detection having an immense impact on patient prognosis. Existing methods are mainly mono-modal data-driven, i.e., imaging (photo) based or clinical outcome result (procedure or action in text form), discarding the complementary information of the multimodal data. Such complexity, or heterogeneity, in real-world datasets results in low generalisability, and thus, reduced diagnostic performance and misclassification. In order to tackle these challenges, this research proposes a new multimodal learning framework called BoneCancerFusionNet that combines medical images with relevant clinical data to detect and classify bone cancer. This framework uses CNNs to extract features from medical images and MLPs to process clinical data. For intrinsic representation, a tailored feature fusion approach that combines details from both modalities, and an optimized preprocessing pipeline that enhances input content with data augmentation and imputation strategies. The algorithm was validated using the Osteosarcoma-Tumor-Assessment Dataset and resulted in 98.79% accuracy, setting the state-of-the-art record when compared to other established methods (SwarmDL and GAN-based) by significant margins. BoneCancerFusionNets is used for its evaluation and is witnessed to make BoneCancerFusionNET a trustworthy methodology to produce false-negative data and apply augmentations to achieve generalizability across precision, recall, and F1 score. The strength of the standalone multimodal data, as a diagnostic, leads to both qualitative and quantitative AI-based solutions for radiology and clinical decision-making.

Keywords

BoneCancerFusionNet, Multimodal Deep Learning, Bone Cancer Detection, Medical Imaging, Clinical Data Integration.

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