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Traditional grading methods rely on manual assessment of pathological slides, which are prone to subjective bias. This paper proposes a deep learning\u2010based multimodal fusion model, named RVCK\u2010net, which integrates hyperspectral imaging (HSI) and pathological images to achieve precise bladder cancer grading. By leveraging spatial and spectral information from both modalities and employing an adaptive fusion mechanism, the proposed model achieves robust and reliable classification. Experimental results show that the method reaches an average accuracy of 94.1% under 10\u2010fold cross\u2010validation, significantly outperforming single\u2010modality approaches and demonstrating improved diagnostic consistency. This study highlights the potential of multimodal deep learning for enhancing early diagnosis and accurate grading of bladder cancer.<\/jats:p>","DOI":"10.1002\/jbio.202500161","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T22:59:56Z","timestamp":1748905196000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hyperspectral Imaging for Predicting Bladder Cancer Grading: A Novel Diagnostic Approach"],"prefix":"10.1002","volume":"18","author":[{"given":"Jinfeng","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Urinary Surgery, the First Affiliated Hospital of Shandong First Medical University &amp; Shandong Provincial Qianfoshan Hospital  Jinan Shandong People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuqing","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Urinary Surgery, the First Affiliated Hospital of Shandong First Medical University &amp; 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