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This study demonstrates that integrating raw MRI scans with extracted radiomic features significantly outperforms unimodal deep learning baselines in classifying brain tumors, achieving 96.13% accuracy through gated fusion strategies. The approach effectively bridges the gap between automated models and clinical reasoning by combining image data with quantitative descriptors like intensity and texture. For the tech community, this validates the utility of multimodal architectures in medical imaging where single-modality inputs often fail to capture the full diagnostic context.
Multimodal Brain Tumour Classification Using Feature Fusion
arXiv:2606.11107v2 Announce Type: replace-cross Abstract: Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement. However, most deep learning models rely on MRI/CT images alone, failing to replicate the clinicians multimodal reasoning. We explore a two-branch multimodal network combining raw MRI scans with 91 extracted radiomic features (intensity, texture, shape, and boundary descriptors) to classify brain tumors into glioma, meningioma, pituitary, and no-tumor. A pre-trained CNN backbone encodes the image stream, whereas a dedicated MLP encodes the radiomic stream. Both streams are fused via concatenation, gated, or bidirectional cross-modal attention strategies. Across nine experimental runs on a balanced 7,200 image dataset, all multimodal configurations outperform unimodal baselines with gated fusion achieving the best accuracy of 96.13%.
Abstract:Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement. However, most deep learning models rely on MRI/CT images alone, failing to replicate the clinicians multimodal reasoning. We explore a two-branch multimodal network combining raw MRI scans with 91 extracted radiomic features (intensity, texture, shape, and boundary descriptors) to classify brain tumors into glioma, meningioma, pituitary, and no-tumor. A pre-trained CNN backbone encodes the image stream, whereas a dedicated MLP encodes the radiomic stream. Both streams are fused via concatenation, gated, or bidirectional cross-modal attention strategies. Across nine experimental runs on a balanced 7,200 image dataset, all multimodal configurations outperform unimodal baselines with gated fusion achieving the best accuracy of 96.13%.
Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2606.11107 [eess.IV]
(or arXiv:2606.11107v2 [eess.IV] for this version)
https://doi.org/10.48550/arXiv.2606.11107 Focus to learn more arXiv-issued DOI via DataCite
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